Please download the zipped folder (homework_2.zip) containing code for Pacman scenarios for you to implement multiagent search algorithms in multiAgents.py file within the folder. For complete details including the deadline, please download “Homework 2 CSE 5120 Multiagent search and CSP x” file attached here.
read the x word file and do the homework. Also the report template is in the word file.
*** You must include .py files in the compressed folder and give 2 files finally (report in x file and the fixed zip file for the homework)
homework_2/autograder.py
# autograder.py
# ————-
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
Don't use plagiarized sources. Get Your Custom Essay on
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# imports from python standard library
import grading
import imp
import optparse
import os
import re
import sys
import projectParams
import random
random.seed(0)
try:
from pacman import GameState
except:
pass
# register arguments and set default values
def readCommand(argv):
parser = optparse.OptionParser(
description=’Run public tests on student code’)
parser.set_defaults(generateSolutions=False, edxOutput=False, gsOutput=False,
muteOutput=False, printTestCase=False, noGraphics=False)
parser.add_option(‘–test-directory’,
dest=’testRoot’,
default=’test_cases’,
help=’Root test directory which contains subdirectories corresponding to each question’)
parser.add_option(‘–student-code’,
dest=’studentCode’,
default=projectParams.STUDENT_CODE_DEFAULT,
help=’comma separated list of student code files’)
parser.add_option(‘–code-directory’,
dest=’codeRoot’,
default=””,
help=’Root directory containing the student and testClass code’)
parser.add_option(‘–test-case-code’,
dest=’testCaseCode’,
default=projectParams.PROJECT_TEST_CLASSES,
help=’class containing testClass classes for this project’)
parser.add_option(‘–generate-solutions’,
dest=’generateSolutions’,
action=’store_true’,
help=’Write solutions generated to .solution file’)
parser.add_option(‘–edx-output’,
dest=’edxOutput’,
action=’store_true’,
help=’Generate edX output files’)
parser.add_option(‘–gradescope-output’,
dest=’gsOutput’,
action=’store_true’,
help=’Generate GradeScope output files’)
parser.add_option(‘–mute’,
dest=’muteOutput’,
action=’store_true’,
help=’Mute output from executing tests’)
parser.add_option(‘–print-tests’, ‘-p’,
dest=’printTestCase’,
action=’store_true’,
help=’Print each test case before running them.’)
parser.add_option(‘–test’, ‘-t’,
dest=’runTest’,
default=None,
help=’Run one particular test. Relative to test root.’)
parser.add_option(‘–question’, ‘-q’,
dest=’gradeQuestion’,
default=None,
help=’Grade one particular question.’)
parser.add_option(‘–no-graphics’,
dest=’noGraphics’,
action=’store_true’,
help=’No graphics display for pacman games.’)
(options, args) = parser.parse_args(argv)
return options
# confirm we should author solution files
def confirmGenerate():
print(‘WARNING: this action will overwrite any solution files.’)
print(‘Are you sure you want to proceed? (yes/no)’)
while True:
ans = sys.stdin.readline().strip()
if ans == ‘yes’:
break
elif ans == ‘no’:
sys.exit(0)
else:
print(‘please answer either “yes” or “no”‘)
# TODO: Fix this so that it tracebacks work correctly
# Looking at source of the traceback module, presuming it works
# the same as the intepreters, it uses co_filename. This is,
# however, a readonly attribute.
def setModuleName(module, filename):
functionType = type(confirmGenerate)
classType = type(optparse.Option)
for i in dir(module):
o = getattr(module, i)
if hasattr(o, ‘__file__’):
continue
if type(o) == functionType:
setattr(o, ‘__file__’, filename)
elif type(o) == classType:
setattr(o, ‘__file__’, filename)
# TODO: assign member __file__’s?
# print i, type(o)
#from cStringIO import StringIO
def loadModuleString(moduleSource):
# Below broken, imp doesn’t believe its being passed a file:
# ValueError: load_module arg#2 should be a file or None
#
#f = StringIO(moduleCodeDict[k])
#tmp = imp.load_module(k, f, k, (“.py”, “r”, imp.PY_SOURCE))
tmp = imp.new_module(k)
exec(moduleCodeDict[k], tmp.__dict__)
setModuleName(tmp, k)
return tmp
import py_compile
def loadModuleFile(moduleName, filePath):
with open(filePath, ‘r’) as f:
return imp.load_module(moduleName, f, “%s.py” % moduleName, (“.py”, “r”, imp.PY_SOURCE))
def readFile(path, root=””):
“Read file from disk at specified path and return as string”
with open(os.path.join(root, path), ‘r’) as handle:
return handle.read()
#######################################################################
# Error Hint Map
#######################################################################
# TODO: use these
ERROR_HINT_MAP = {
‘q1’: {
““: “””
We noticed that your project threw an IndexError on q1.
While many things may cause this, it may have been from
assuming a certain number of successors from a state space
or assuming a certain number of actions available from a given
state. Try making your code more general (no hardcoded indices)
and submit again!
“””
},
‘q3’: {
““: “””
We noticed that your project threw an AttributeError on q3.
While many things may cause this, it may have been from assuming
a certain size or structure to the state space. For example, if you have
a line of code assuming that the state is (x, y) and we run your code
on a state space with (x, y, z), this error could be thrown. Try
making your code more general and submit again!
“””
}
}
import pprint
def splitStrings(d):
d2 = dict(d)
for k in d:
if k[0:2] == “__”:
del d2[k]
continue
if d2[k].find(“\n”) >= 0:
d2[k] = d2[k].split(“\n”)
return d2
def printTest(testDict, solutionDict):
pp = pprint.PrettyPrinter(indent=4)
print(“Test case:”)
for line in testDict[“__raw_lines__”]:
print(” |”, line)
print(“Solution:”)
for line in solutionDict[“__raw_lines__”]:
print(” |”, line)
def runTest(testName, moduleDict, printTestCase=False, display=None):
import testParser
import testClasses
for module in moduleDict:
setattr(sys.modules[__name__], module, moduleDict[module])
testDict = testParser.TestParser(testName + “.test”).parse()
solutionDict = testParser.TestParser(testName + “.solution”).parse()
test_out_file = os.path.join(‘%s.test_output’ % testName)
testDict[‘test_out_file’] = test_out_file
testClass = getattr(projectTestClasses, testDict[‘class’])
questionClass = getattr(testClasses, ‘Question’)
question = questionClass({‘max_points’: 0}, display)
testCase = testClass(question, testDict)
if printTestCase:
printTest(testDict, solutionDict)
# This is a fragile hack to create a stub grades object
grades = grading.Grades(projectParams.PROJECT_NAME, [(None, 0)])
testCase.execute(grades, moduleDict, solutionDict)
# returns all the tests you need to run in order to run question
def getDepends(testParser, testRoot, question):
allDeps = [question]
questionDict = testParser.TestParser(
os.path.join(testRoot, question, ‘CONFIG’)).parse()
if ‘depends’ in questionDict:
depends = questionDict[‘depends’].split()
for d in depends:
# run dependencies first
allDeps = getDepends(testParser, testRoot, d) + allDeps
return allDeps
# get list of questions to grade
def getTestSubdirs(testParser, testRoot, questionToGrade):
problemDict = testParser.TestParser(
os.path.join(testRoot, ‘CONFIG’)).parse()
if questionToGrade != None:
questions = getDepends(testParser, testRoot, questionToGrade)
if len(questions) > 1:
print(‘Note: due to dependencies, the following tests will be run: %s’ %
‘ ‘.join(questions))
return questions
if ‘order’ in problemDict:
return problemDict[‘order’].split()
return sorted(os.listdir(testRoot))
# evaluate student code
def evaluate(generateSolutions, testRoot, moduleDict, exceptionMap=ERROR_HINT_MAP,
edxOutput=False, muteOutput=False, gsOutput=False,
printTestCase=False, questionToGrade=None, display=None):
# imports of testbench code. note that the testClasses import must follow
# the import of student code due to dependencies
import testParser
import testClasses
for module in moduleDict:
setattr(sys.modules[__name__], module, moduleDict[module])
questions = []
questionDicts = {}
test_subdirs = getTestSubdirs(testParser, testRoot, questionToGrade)
for q in test_subdirs:
subdir_path = os.path.join(testRoot, q)
if not os.path.isdir(subdir_path) or q[0] == ‘.’:
continue
# create a question object
questionDict = testParser.TestParser(
os.path.join(subdir_path, ‘CONFIG’)).parse()
questionClass = getattr(testClasses, questionDict[‘class’])
question = questionClass(questionDict, display)
questionDicts[q] = questionDict
# load test cases into question
tests = [t for t in os.listdir(
subdir_path) if re.match(‘[^#~.].*\.test\Z’, t)]
tests = [re.match(‘(.*)\.test\Z’, t).group(1) for t in tests]
for t in sorted(tests):
test_file = os.path.join(subdir_path, ‘%s.test’ % t)
solution_file = os.path.join(subdir_path, ‘%s.solution’ % t)
test_out_file = os.path.join(subdir_path, ‘%s.test_output’ % t)
testDict = testParser.TestParser(test_file).parse()
if testDict.get(“disabled”, “false”).lower() == “true”:
continue
testDict[‘test_out_file’] = test_out_file
testClass = getattr(projectTestClasses, testDict[‘class’])
testCase = testClass(question, testDict)
def makefun(testCase, solution_file):
if generateSolutions:
# write solution file to disk
return lambda grades: testCase.writeSolution(moduleDict, solution_file)
else:
# read in solution dictionary and pass as an argument
testDict = testParser.TestParser(test_file).parse()
solutionDict = testParser.TestParser(solution_file).parse()
if printTestCase:
return lambda grades: printTest(testDict, solutionDict) or testCase.execute(grades, moduleDict, solutionDict)
else:
return lambda grades: testCase.execute(grades, moduleDict, solutionDict)
question.addTestCase(testCase, makefun(testCase, solution_file))
# Note extra function is necessary for scoping reasons
def makefun(question):
return lambda grades: question.execute(grades)
setattr(sys.modules[__name__], q, makefun(question))
questions.append((q, question.getMaxPoints()))
grades = grading.Grades(projectParams.PROJECT_NAME, questions,
gsOutput=gsOutput, edxOutput=edxOutput, muteOutput=muteOutput)
if questionToGrade == None:
for q in questionDicts:
for prereq in questionDicts[q].get(‘depends’, ”).split():
grades.addPrereq(q, prereq)
grades.grade(sys.modules[__name__], bonusPic=projectParams.BONUS_PIC)
return grades.points
def getDisplay(graphicsByDefault, options=None):
graphics = graphicsByDefault
if options is not None and options.noGraphics:
graphics = False
if graphics:
try:
import graphicsDisplay
return graphicsDisplay.PacmanGraphics(1, frameTime=.05)
except ImportError:
pass
import textDisplay
return textDisplay.NullGraphics()
if __name__ == ‘__main__’:
options = readCommand(sys.argv)
if options.generateSolutions:
confirmGenerate()
codePaths = options.studentCode.split(‘,’)
# moduleCodeDict = {}
# for cp in codePaths:
# moduleName = re.match(‘.*?([^/]*)\.py’, cp).group(1)
# moduleCodeDict[moduleName] = readFile(cp, root=options.codeRoot)
# moduleCodeDict[‘projectTestClasses’] = readFile(options.testCaseCode, root=options.codeRoot)
# moduleDict = loadModuleDict(moduleCodeDict)
moduleDict = {}
for cp in codePaths:
moduleName = re.match(‘.*?([^/]*)\.py’, cp).group(1)
moduleDict[moduleName] = loadModuleFile(
moduleName, os.path.join(options.codeRoot, cp))
moduleName = re.match(‘.*?([^/]*)\.py’, options.testCaseCode).group(1)
moduleDict[‘projectTestClasses’] = loadModuleFile(
moduleName, os.path.join(options.codeRoot, options.testCaseCode))
if options.runTest != None:
runTest(options.runTest, moduleDict, printTestCase=options.printTestCase,
display=getDisplay(True, options))
else:
evaluate(options.generateSolutions, options.testRoot, moduleDict,
gsOutput=options.gsOutput,
edxOutput=options.edxOutput, muteOutput=options.muteOutput, printTestCase=options.printTestCase,
questionToGrade=options.gradeQuestion, display=getDisplay(options.gradeQuestion != None, options))
homework_2/game.py
# game.py
# ——-
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
# game.py
# ——-
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
from util import *
import time
import os
import traceback
import sys
#######################
# Parts worth reading #
#######################
class Agent:
“””
An agent must define a getAction method, but may also define the
following methods which will be called if they exist:
def registerInitialState(self, state): # inspects the starting state
“””
def __init__(self, index=0):
self.index = index
def getAction(self, state):
“””
The Agent will receive a GameState (from either {pacman, capture, sonar}.py) and
must return an action from Directions.{North, South, East, West, Stop}
“””
raiseNotDefined()
class Directions:
NORTH = ‘North’
SOUTH = ‘South’
EAST = ‘East’
WEST = ‘West’
STOP = ‘Stop’
LEFT = {NORTH: WEST,
SOUTH: EAST,
EAST: NORTH,
WEST: SOUTH,
STOP: STOP}
RIGHT = dict([(y, x) for x, y in list(LEFT.items())])
REVERSE = {NORTH: SOUTH,
SOUTH: NORTH,
EAST: WEST,
WEST: EAST,
STOP: STOP}
class Configuration:
“””
A Configuration holds the (x,y) coordinate of a character, along with its
traveling direction.
The convention for positions, like a graph, is that (0,0) is the lower left corner, x increases
horizontally and y increases vertically. Therefore, north is the direction of increasing y, or (0,1).
“””
def __init__(self, pos, direction):
self.pos = pos
self.direction = direction
def getPosition(self):
return (self.pos)
def getDirection(self):
return self.direction
def isInteger(self):
x, y = self.pos
return x == int(x) and y == int(y)
def __eq__(self, other):
if other == None:
return False
return (self.pos == other.pos and self.direction == other.direction)
def __hash__(self):
x = hash(self.pos)
y = hash(self.direction)
return hash(x + 13 * y)
def __str__(self):
return “(x,y)=”+str(self.pos)+”, “+str(self.direction)
def generateSuccessor(self, vector):
“””
Generates a new configuration reached by translating the current
configuration by the action vector. This is a low-level call and does
not attempt to respect the legality of the movement.
Actions are movement vectors.
“””
x, y = self.pos
dx, dy = vector
direction = Actions.vectorToDirection(vector)
if direction == Directions.STOP:
direction = self.direction # There is no stop direction
return Configuration((x + dx, y+dy), direction)
class AgentState:
“””
AgentStates hold the state of an agent (configuration, speed, scared, etc).
“””
def __init__(self, startConfiguration, isPacman):
self.start = startConfiguration
self.configuration = startConfiguration
self.isPacman = isPacman
self.scaredTimer = 0
# state below potentially used for contest only
self.numCarrying = 0
self.numReturned = 0
def __str__(self):
if self.isPacman:
return “Pacman: ” + str(self.configuration)
else:
return “Ghost: ” + str(self.configuration)
def __eq__(self, other):
if other == None:
return False
return self.configuration == other.configuration and self.scaredTimer == other.scaredTimer
def __hash__(self):
return hash(hash(self.configuration) + 13 * hash(self.scaredTimer))
def copy(self):
state = AgentState(self.start, self.isPacman)
state.configuration = self.configuration
state.scaredTimer = self.scaredTimer
state.numCarrying = self.numCarrying
state.numReturned = self.numReturned
return state
def getPosition(self):
if self.configuration == None:
return None
return self.configuration.getPosition()
def getDirection(self):
return self.configuration.getDirection()
class Grid:
“””
A 2-dimensional array of objects backed by a list of lists. Data is accessed
via grid[x][y] where (x,y) are positions on a Pacman map with x horizontal,
y vertical and the origin (0,0) in the bottom left corner.
The __str__ method constructs an output that is oriented like a pacman board.
“””
def __init__(self, width, height, initialValue=False, bitRepresentation=None):
if initialValue not in [False, True]:
raise Exception(‘Grids can only contain booleans’)
self.CELLS_PER_INT = 30
self.width = width
self.height = height
self.data = [[initialValue for y in range(
height)] for x in range(width)]
if bitRepresentation:
self._unpackBits(bitRepresentation)
def __getitem__(self, i):
return self.data[i]
def __setitem__(self, key, item):
self.data[key] = item
def __str__(self):
out = [[str(self.data[x][y])[0] for x in range(self.width)]
for y in range(self.height)]
out.reverse()
return ‘\n’.join([”.join(x) for x in out])
def __eq__(self, other):
if other == None:
return False
return self.data == other.data
def __hash__(self):
# return hash(str(self))
base = 1
h = 0
for l in self.data:
for i in l:
if i:
h += base
base *= 2
return hash(h)
def copy(self):
g = Grid(self.width, self.height)
g.data = [x[:] for x in self.data]
return g
def deepCopy(self):
return self.copy()
def shallowCopy(self):
g = Grid(self.width, self.height)
g.data = self.data
return g
def count(self, item=True):
return sum([x.count(item) for x in self.data])
def asList(self, key=True):
list = []
for x in range(self.width):
for y in range(self.height):
if self[x][y] == key:
list.append((x, y))
return list
def packBits(self):
“””
Returns an efficient int list representation
(width, height, bitPackedInts…)
“””
bits = [self.width, self.height]
currentInt = 0
for i in range(self.height * self.width):
bit = self.CELLS_PER_INT – (i % self.CELLS_PER_INT) – 1
x, y = self._cellIndexToPosition(i)
if self[x][y]:
currentInt += 2 ** bit
if (i + 1) % self.CELLS_PER_INT == 0:
bits.append(currentInt)
currentInt = 0
bits.append(currentInt)
return tuple(bits)
def _cellIndexToPosition(self, index):
x = index / self.height
y = index % self.height
return x, y
def _unpackBits(self, bits):
“””
Fills in data from a bit-level representation
“””
cell = 0
for packed in bits:
for bit in self._unpackInt(packed, self.CELLS_PER_INT):
if cell == self.width * self.height:
break
x, y = self._cellIndexToPosition(cell)
self[x][y] = bit
cell += 1
def _unpackInt(self, packed, size):
bools = []
if packed < 0:
raise ValueError("must be a positive integer")
for i in range(size):
n = 2 ** (self.CELLS_PER_INT - i - 1)
if packed >= n:
bools.append(True)
packed -= n
else:
bools.append(False)
return bools
def reconstituteGrid(bitRep):
if type(bitRep) is not type((1, 2)):
return bitRep
width, height = bitRep[:2]
return Grid(width, height, bitRepresentation=bitRep[2:])
####################################
# Parts you shouldn’t have to read #
####################################
class Actions:
“””
A collection of static methods for manipulating move actions.
“””
# Directions
_directions = {Directions.WEST: (-1, 0),
Directions.STOP: (0, 0),
Directions.EAST: (1, 0),
Directions.NORTH: (0, 1),
Directions.SOUTH: (0, -1)}
_directionsAsList = [(‘West’, (-1, 0)), (‘Stop’, (0, 0)), (‘East’, (1, 0)), (‘North’, (0, 1)), (‘South’, (0, -1))]
TOLERANCE = .001
def reverseDirection(action):
if action == Directions.NORTH:
return Directions.SOUTH
if action == Directions.SOUTH:
return Directions.NORTH
if action == Directions.EAST:
return Directions.WEST
if action == Directions.WEST:
return Directions.EAST
return action
reverseDirection = staticmethod(reverseDirection)
def vectorToDirection(vector):
dx, dy = vector
if dy > 0:
return Directions.NORTH
if dy < 0:
return Directions.SOUTH
if dx < 0:
return Directions.WEST
if dx > 0:
return Directions.EAST
return Directions.STOP
vectorToDirection = staticmethod(vectorToDirection)
def directionToVector(direction, speed=1.0):
dx, dy = Actions._directions[direction]
return (dx * speed, dy * speed)
directionToVector = staticmethod(directionToVector)
def getPossibleActions(config, walls):
possible = []
x, y = config.pos
x_int, y_int = int(x + 0.5), int(y + 0.5)
# In between grid points, all agents must continue straight
if (abs(x – x_int) + abs(y – y_int) > Actions.TOLERANCE):
return [config.getDirection()]
for dir, vec in Actions._directionsAsList:
dx, dy = vec
next_y = y_int + dy
next_x = x_int + dx
if not walls[next_x][next_y]:
possible.append(dir)
return possible
getPossibleActions = staticmethod(getPossibleActions)
def getLegalNeighbors(position, walls):
x, y = position
x_int, y_int = int(x + 0.5), int(y + 0.5)
neighbors = []
for dir, vec in Actions._directionsAsList:
dx, dy = vec
next_x = x_int + dx
if next_x < 0 or next_x == walls.width:
continue
next_y = y_int + dy
if next_y < 0 or next_y == walls.height:
continue
if not walls[next_x][next_y]:
neighbors.append((next_x, next_y))
return neighbors
getLegalNeighbors = staticmethod(getLegalNeighbors)
def getSuccessor(position, action):
dx, dy = Actions.directionToVector(action)
x, y = position
return (x + dx, y + dy)
getSuccessor = staticmethod(getSuccessor)
class GameStateData:
def __init__(self, prevState=None):
"""
Generates a new data packet by copying information from its predecessor.
"""
if prevState != None:
self.food = prevState.food.shallowCopy()
self.capsules = prevState.capsules[:]
self.agentStates = self.copyAgentStates(prevState.agentStates)
self.layout = prevState.layout
self._eaten = prevState._eaten
self.score = prevState.score
self._foodEaten = None
self._foodAdded = None
self._capsuleEaten = None
self._agentMoved = None
self._lose = False
self._win = False
self.scoreChange = 0
def deepCopy(self):
state = GameStateData(self)
state.food = self.food.deepCopy()
state.layout = self.layout.deepCopy()
state._agentMoved = self._agentMoved
state._foodEaten = self._foodEaten
state._foodAdded = self._foodAdded
state._capsuleEaten = self._capsuleEaten
return state
def copyAgentStates(self, agentStates):
copiedStates = []
for agentState in agentStates:
copiedStates.append(agentState.copy())
return copiedStates
def __eq__(self, other):
"""
Allows two states to be compared.
"""
if other == None:
return False
# TODO Check for type of other
if not self.agentStates == other.agentStates:
return False
if not self.food == other.food:
return False
if not self.capsules == other.capsules:
return False
if not self.score == other.score:
return False
return True
def __hash__(self):
"""
Allows states to be keys of dictionaries.
"""
for i, state in enumerate(self.agentStates):
try:
int(hash(state))
except TypeError as e:
print(e)
# hash(state)
return int((hash(tuple(self.agentStates)) + 13*hash(self.food) + 113 * hash(tuple(self.capsules)) + 7 * hash(self.score)) % 1048575)
def __str__(self):
width, height = self.layout.width, self.layout.height
map = Grid(width, height)
if type(self.food) == type((1, 2)):
self.food = reconstituteGrid(self.food)
for x in range(width):
for y in range(height):
food, walls = self.food, self.layout.walls
map[x][y] = self._foodWallStr(food[x][y], walls[x][y])
for agentState in self.agentStates:
if agentState == None:
continue
if agentState.configuration == None:
continue
x, y = [int(i) for i in nearestPoint(agentState.configuration.pos)]
agent_dir = agentState.configuration.direction
if agentState.isPacman:
map[x][y] = self._pacStr(agent_dir)
else:
map[x][y] = self._ghostStr(agent_dir)
for x, y in self.capsules:
map[x][y] = 'o'
return str(map) + ("\nScore: %d\n" % self.score)
def _foodWallStr(self, hasFood, hasWall):
if hasFood:
return '.'
elif hasWall:
return '%'
else:
return ' '
def _pacStr(self, dir):
if dir == Directions.NORTH:
return 'v'
if dir == Directions.SOUTH:
return '^'
if dir == Directions.WEST:
return '>‘
return ‘<'
def _ghostStr(self, dir):
return 'G'
if dir == Directions.NORTH:
return 'M'
if dir == Directions.SOUTH:
return 'W'
if dir == Directions.WEST:
return '3'
return 'E'
def initialize(self, layout, numGhostAgents):
"""
Creates an initial game state from a layout array (see layout.py).
"""
self.food = layout.food.copy()
#self.capsules = []
self.capsules = layout.capsules[:]
self.layout = layout
self.score = 0
self.scoreChange = 0
self.agentStates = []
numGhosts = 0
for isPacman, pos in layout.agentPositions:
if not isPacman:
if numGhosts == numGhostAgents:
continue # Max ghosts reached already
else:
numGhosts += 1
self.agentStates.append(AgentState(
Configuration(pos, Directions.STOP), isPacman))
self._eaten = [False for a in self.agentStates]
try:
import boinc
_BOINC_ENABLED = True
except:
_BOINC_ENABLED = False
class Game:
"""
The Game manages the control flow, soliciting actions from agents.
"""
def __init__(self, agents, display, rules, startingIndex=0, muteAgents=False, catchExceptions=False):
self.agentCrashed = False
self.agents = agents
self.display = display
self.rules = rules
self.startingIndex = startingIndex
self.gameOver = False
self.muteAgents = muteAgents
self.catchExceptions = catchExceptions
self.moveHistory = []
self.totalAgentTimes = [0 for agent in agents]
self.totalAgentTimeWarnings = [0 for agent in agents]
self.agentTimeout = False
import io
self.agentOutput = [io.StringIO() for agent in agents]
def getProgress(self):
if self.gameOver:
return 1.0
else:
return self.rules.getProgress(self)
def _agentCrash(self, agentIndex, quiet=False):
"Helper method for handling agent crashes"
if not quiet:
traceback.print_exc()
self.gameOver = True
self.agentCrashed = True
self.rules.agentCrash(self, agentIndex)
OLD_STDOUT = None
OLD_STDERR = None
def mute(self, agentIndex):
if not self.muteAgents:
return
global OLD_STDOUT, OLD_STDERR
import io
OLD_STDOUT = sys.stdout
OLD_STDERR = sys.stderr
sys.stdout = self.agentOutput[agentIndex]
sys.stderr = self.agentOutput[agentIndex]
def unmute(self):
if not self.muteAgents:
return
global OLD_STDOUT, OLD_STDERR
# Revert stdout/stderr to originals
sys.stdout = OLD_STDOUT
sys.stderr = OLD_STDERR
def run(self):
"""
Main control loop for game play.
"""
self.display.initialize(self.state.data)
self.numMoves = 0
# self.display.initialize(self.state.makeObservation(1).data)
# inform learning agents of the game start
for i in range(len(self.agents)):
agent = self.agents[i]
if not agent:
self.mute(i)
# this is a null agent, meaning it failed to load
# the other team wins
print("Agent %d failed to load" % i, file=sys.stderr)
self.unmute()
self._agentCrash(i, quiet=True)
return
if ("registerInitialState" in dir(agent)):
self.mute(i)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(
agent.registerInitialState, int(self.rules.getMaxStartupTime(i)))
try:
start_time = time.time()
timed_func(self.state.deepCopy())
time_taken = time.time() - start_time
self.totalAgentTimes[i] += time_taken
except TimeoutFunctionException:
print("Agent %d ran out of time on startup!" %
i, file=sys.stderr)
self.unmute()
self.agentTimeout = True
self._agentCrash(i, quiet=True)
return
except Exception as data:
self._agentCrash(i, quiet=False)
self.unmute()
return
else:
agent.registerInitialState(self.state.deepCopy())
# TODO: could this exceed the total time
self.unmute()
agentIndex = self.startingIndex
numAgents = len(self.agents)
while not self.gameOver:
# Fetch the next agent
agent = self.agents[agentIndex]
move_time = 0
skip_action = False
# Generate an observation of the state
if 'observationFunction' in dir(agent):
self.mute(agentIndex)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(agent.observationFunction, int(
self.rules.getMoveTimeout(agentIndex)))
try:
start_time = time.time()
observation = timed_func(self.state.deepCopy())
except TimeoutFunctionException:
skip_action = True
move_time += time.time() - start_time
self.unmute()
except Exception as data:
self._agentCrash(agentIndex, quiet=False)
self.unmute()
return
else:
observation = agent.observationFunction(
self.state.deepCopy())
self.unmute()
else:
observation = self.state.deepCopy()
# Solicit an action
action = None
self.mute(agentIndex)
if self.catchExceptions:
try:
timed_func = TimeoutFunction(agent.getAction, int(
self.rules.getMoveTimeout(agentIndex)) - int(move_time))
try:
start_time = time.time()
if skip_action:
raise TimeoutFunctionException()
action = timed_func(observation)
except TimeoutFunctionException:
print("Agent %d timed out on a single move!" %
agentIndex, file=sys.stderr)
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
move_time += time.time() - start_time
if move_time > self.rules.getMoveWarningTime(agentIndex):
self.totalAgentTimeWarnings[agentIndex] += 1
print(“Agent %d took too long to make a move! This is warning %d” % (
agentIndex, self.totalAgentTimeWarnings[agentIndex]), file=sys.stderr)
if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
print(“Agent %d exceeded the maximum number of warnings: %d” % (
agentIndex, self.totalAgentTimeWarnings[agentIndex]), file=sys.stderr)
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
self.totalAgentTimes[agentIndex] += move_time
# print “Agent: %d, time: %f, total: %f” % (agentIndex, move_time, self.totalAgentTimes[agentIndex])
if self.totalAgentTimes[agentIndex] > self.rules.getMaxTotalTime(agentIndex):
print(“Agent %d ran out of time! (time: %1.2f)” % (
agentIndex, self.totalAgentTimes[agentIndex]), file=sys.stderr)
self.agentTimeout = True
self._agentCrash(agentIndex, quiet=True)
self.unmute()
return
self.unmute()
except Exception as data:
self._agentCrash(agentIndex)
self.unmute()
return
else:
action = agent.getAction(observation)
self.unmute()
# Execute the action
self.moveHistory.append((agentIndex, action))
if self.catchExceptions:
try:
self.state = self.state.generateSuccessor(
agentIndex, action)
except Exception as data:
self.mute(agentIndex)
self._agentCrash(agentIndex)
self.unmute()
return
else:
self.state = self.state.generateSuccessor(agentIndex, action)
# Change the display
self.display.update(self.state.data)
###idx = agentIndex – agentIndex % 2 + 1
###self.display.update( self.state.makeObservation(idx).data )
# Allow for game specific conditions (winning, losing, etc.)
self.rules.process(self.state, self)
# Track progress
if agentIndex == numAgents + 1:
self.numMoves += 1
# Next agent
agentIndex = (agentIndex + 1) % numAgents
if _BOINC_ENABLED:
boinc.set_fraction_done(self.getProgress())
# inform a learning agent of the game result
for agentIndex, agent in enumerate(self.agents):
if “final” in dir(agent):
try:
self.mute(agentIndex)
agent.final(self.state)
self.unmute()
except Exception as data:
if not self.catchExceptions:
raise
self._agentCrash(agentIndex)
self.unmute()
return
self.display.finish()
homework_2/ghostAgents.py
# ghostAgents.py
# ————–
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from game import Agent
from game import Actions
from game import Directions
import random
from util import manhattanDistance
import util
class GhostAgent(Agent):
def __init__(self, index):
self.index = index
def getAction(self, state):
dist = self.getDistribution(state)
if len(dist) == 0:
return Directions.STOP
else:
return util.chooseFromDistribution(dist)
def getDistribution(self, state):
“Returns a Counter encoding a distribution over actions from the provided state.”
util.raiseNotDefined()
class RandomGhost(GhostAgent):
“A ghost that chooses a legal action uniformly at random.”
def getDistribution(self, state):
dist = util.Counter()
for a in state.getLegalActions(self.index):
dist[a] = 1.0
dist.normalize()
return dist
class DirectionalGhost(GhostAgent):
“A ghost that prefers to rush Pacman, or flee when scared.”
def __init__(self, index, prob_attack=0.8, prob_scaredFlee=0.8):
self.index = index
self.prob_attack = prob_attack
self.prob_scaredFlee = prob_scaredFlee
def getDistribution(self, state):
# Read variables from state
ghostState = state.getGhostState(self.index)
legalActions = state.getLegalActions(self.index)
pos = state.getGhostPosition(self.index)
isScared = ghostState.scaredTimer > 0
speed = 1
if isScared:
speed = 0.5
actionVectors = [Actions.directionToVector(
a, speed) for a in legalActions]
newPositions = [(pos[0]+a[0], pos[1]+a[1]) for a in actionVectors]
pacmanPosition = state.getPacmanPosition()
# Select best actions given the state
distancesToPacman = [manhattanDistance(
pos, pacmanPosition) for pos in newPositions]
if isScared:
bestScore = max(distancesToPacman)
bestProb = self.prob_scaredFlee
else:
bestScore = min(distancesToPacman)
bestProb = self.prob_attack
bestActions = [action for action, distance in zip(
legalActions, distancesToPacman) if distance == bestScore]
# Construct distribution
dist = util.Counter()
for a in bestActions:
dist[a] = bestProb / len(bestActions)
for a in legalActions:
dist[a] += (1-bestProb) / len(legalActions)
dist.normalize()
return dist
homework_2/grading.py
# grading.py
# ———-
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
“Common code for autograders”
import cgi
import time
import sys
import json
import traceback
import pdb
from collections import defaultdict
import util
class Grades:
“A data structure for project grades, along with formatting code to display them”
def __init__(self, projectName, questionsAndMaxesList,
gsOutput=False, edxOutput=False, muteOutput=False):
“””
Defines the grading scheme for a project
projectName: project name
questionsAndMaxesDict: a list of (question name, max points per question)
“””
self.questions = [el[0] for el in questionsAndMaxesList]
self.maxes = dict(questionsAndMaxesList)
self.points = Counter()
self.messages = dict([(q, []) for q in self.questions])
self.project = projectName
self.start = time.localtime()[1:6]
self.sane = True # Sanity checks
self.currentQuestion = None # Which question we’re grading
self.edxOutput = edxOutput
self.gsOutput = gsOutput # GradeScope output
self.mute = muteOutput
self.prereqs = defaultdict(set)
# print ‘Autograder transcript for %s’ % self.project
print(‘Starting on %d-%d at %d:%02d:%02d’ % self.start)
def addPrereq(self, question, prereq):
self.prereqs[question].add(prereq)
def grade(self, gradingModule, exceptionMap={}, bonusPic=False):
“””
Grades each question
gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])
“””
completedQuestions = set([])
for q in self.questions:
print(‘\nQuestion %s’ % q)
print(‘=’ * (9 + len(q)))
print()
self.currentQuestion = q
incompleted = self.prereqs[q].difference(completedQuestions)
if len(incompleted) > 0:
prereq = incompleted.pop()
print(“””*** NOTE: Make sure to complete Question %s before working on Question %s,
*** because Question %s builds upon your answer for Question %s.
“”” % (prereq, q, q, prereq))
continue
if self.mute:
util.mutePrint()
try:
util.TimeoutFunction(getattr(gradingModule, q), 1800)(
self) # Call the question’s function
# TimeoutFunction(getattr(gradingModule, q),1200)(self) # Call the question’s function
except Exception as inst:
self.addExceptionMessage(q, inst, traceback)
self.addErrorHints(exceptionMap, inst, q[1])
except:
self.fail(‘FAIL: Terminated with a string exception.’)
finally:
if self.mute:
util.unmutePrint()
if self.points[q] >= self.maxes[q]:
completedQuestions.add(q)
print(‘\n### Question %s: %d/%d ###\n’ %
(q, self.points[q], self.maxes[q]))
print(‘\nFinished at %d:%02d:%02d’ % time.localtime()[3:6])
print(“\nProvisional grades\n==================”)
for q in self.questions:
print(‘Question %s: %d/%d’ % (q, self.points[q], self.maxes[q]))
print(‘——————‘)
print(‘Total: %d/%d’ %
(self.points.totalCount(), sum(self.maxes.values())))
if bonusPic and self.points.totalCount() == 25:
print(“””
ALL HAIL GRANDPAC.
LONG LIVE THE GHOSTBUSTING KING.
— —- —
| \ / + \ / |
| + \–/ \–/ + |
| + + |
| + + + |
@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
\ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
\ / @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
V \ @@@@@@@@@@@@@@@@@@@@@@@@@@@@
\ / @@@@@@@@@@@@@@@@@@@@@@@@@@
V @@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@
/\ @@@@@@@@@@@@@@@@@@@@@@
/ \ @@@@@@@@@@@@@@@@@@@@@@@@@
/\ / @@@@@@@@@@@@@@@@@@@@@@@@@@@
/ \ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
/ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@
“””)
print(“””
Your grades are NOT yet registered. To register your grades, make sure
to follow your instructor’s guidelines to receive credit on your project.
“””)
if self.edxOutput:
self.produceOutput()
if self.gsOutput:
self.produceGradeScopeOutput()
def addExceptionMessage(self, q, inst, traceback):
“””
Method to format the exception message, this is more complicated because
we need to cgi.escape the traceback but wrap the exception in a
tag
"""
self.fail('FAIL: Exception raised: %s' % inst)
self.addMessage('')
for line in traceback.format_exc().split('\n'):
self.addMessage(line)
def addErrorHints(self, exceptionMap, errorInstance, questionNum):
typeOf = str(type(errorInstance))
questionName = 'q' + questionNum
errorHint = ''
# question specific error hints
if exceptionMap.get(questionName):
questionMap = exceptionMap.get(questionName)
if (questionMap.get(typeOf)):
errorHint = questionMap.get(typeOf)
# fall back to general error messages if a question specific
# one does not exist
if (exceptionMap.get(typeOf)):
errorHint = exceptionMap.get(typeOf)
# dont include the HTML if we have no error hint
if not errorHint:
return ''
for line in errorHint.split('\n'):
self.addMessage(line)
def produceGradeScopeOutput(self):
out_dct = {}
# total of entire submission
total_possible = sum(self.maxes.values())
total_score = sum(self.points.values())
out_dct['score'] = total_score
out_dct['max_score'] = total_possible
out_dct['output'] = "Total score (%d / %d)" % (
total_score, total_possible)
# individual tests
tests_out = []
for name in self.questions:
test_out = {}
# test name
test_out['name'] = name
# test score
test_out['score'] = self.points[name]
test_out['max_score'] = self.maxes[name]
# others
is_correct = self.points[name] >= self.maxes[name]
test_out['output'] = " Question {num} ({points}/{max}) {correct}".format(
num=(name[1] if len(name) == 2 else name),
points=test_out['score'],
max=test_out['max_score'],
correct=('X' if not is_correct else ''),
)
test_out['tags'] = []
tests_out.append(test_out)
out_dct['tests'] = tests_out
# file output
with open('gradescope_response.json', 'w') as outfile:
json.dump(out_dct, outfile)
return
def produceOutput(self):
edxOutput = open('edx_response.html', 'w')
edxOutput.write("")
# first sum
total_possible = sum(self.maxes.values())
total_score = sum(self.points.values())
checkOrX = '
'
if (total_score >= total_possible):
checkOrX = '
'
header = """
Total score ({total_score} / {total_possible})
""".format(total_score=total_score,
total_possible=total_possible,
checkOrX=checkOrX
)
edxOutput.write(header)
for q in self.questions:
if len(q) == 2:
name = q[1]
else:
name = q
checkOrX = '
'
if (self.points[q] >= self.maxes[q]):
checkOrX = '
'
#messages = '\n
\n'.join(self.messages[q])
messages = "
%s
" % '\n'.join(self.messages[q])
output = """
Question {q} ({points}/{max}) {checkOrX}
{messages}
""".format(q=name,
max=self.maxes[q],
messages=messages,
checkOrX=checkOrX,
points=self.points[q]
)
# print "*** output for Question %s " % q[1]
# print output
edxOutput.write(output)
edxOutput.write("
")
edxOutput.close()
edxOutput = open('edx_grade', 'w')
edxOutput.write(str(self.points.totalCount()))
edxOutput.close()
def fail(self, message, raw=False):
"Sets sanity check bit to false and outputs a message"
self.sane = False
self.assignZeroCredit()
self.addMessage(message, raw)
def assignZeroCredit(self):
self.points[self.currentQuestion] = 0
def addPoints(self, amt):
self.points[self.currentQuestion] += amt
def deductPoints(self, amt):
self.points[self.currentQuestion] -= amt
def assignFullCredit(self, message="", raw=False):
self.points[self.currentQuestion] = self.maxes[self.currentQuestion]
if message != "":
self.addMessage(message, raw)
def addMessage(self, message, raw=False):
if not raw:
# We assume raw messages, formatted for HTML, are printed separately
if self.mute:
util.unmutePrint()
print('*** ' + message)
if self.mute:
util.mutePrint()
message = cgi.escape(message)
self.messages[self.currentQuestion].append(message)
def addMessageToEmail(self, message):
print("WARNING**** addMessageToEmail is deprecated %s" % message)
for line in message.split('\n'):
pass
# print '%%% ' + line + ' %%%'
# self.messages[self.currentQuestion].append(line)
class Counter(dict):
"""
Dict with default 0
"""
def __getitem__(self, idx):
try:
return dict.__getitem__(self, idx)
except KeyError:
return 0
def totalCount(self):
"""
Returns the sum of counts for all keys.
"""
return sum(self.values())
homework_2/graphicsDisplay.py
# graphicsDisplay.py
# ------------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from graphicsUtils import *
import math
import time
from game import Directions
###########################
# GRAPHICS DISPLAY CODE #
###########################
# Most code by Dan Klein and John Denero written or rewritten for cs188, UC Berkeley.
# Some code from a Pacman implementation by LiveWires, and used / modified with permission.
DEFAULT_GRID_SIZE = 30.0
INFO_PANE_HEIGHT = 35
BACKGROUND_COLOR = formatColor(0, 0, 0)
WALL_COLOR = formatColor(0.0/255.0, 51.0/255.0, 255.0/255.0)
INFO_PANE_COLOR = formatColor(.4, .4, 0)
SCORE_COLOR = formatColor(.9, .9, .9)
PACMAN_OUTLINE_WIDTH = 2
PACMAN_CAPTURE_OUTLINE_WIDTH = 4
GHOST_COLORS = []
GHOST_COLORS.append(formatColor(.9, 0, 0)) # Red
GHOST_COLORS.append(formatColor(0, .3, .9)) # Blue
GHOST_COLORS.append(formatColor(.98, .41, .07)) # Orange
GHOST_COLORS.append(formatColor(.1, .75, .7)) # Green
GHOST_COLORS.append(formatColor(1.0, 0.6, 0.0)) # Yellow
GHOST_COLORS.append(formatColor(.4, 0.13, 0.91)) # Purple
TEAM_COLORS = GHOST_COLORS[:2]
GHOST_SHAPE = [
(0, 0.3),
(0.25, 0.75),
(0.5, 0.3),
(0.75, 0.75),
(0.75, -0.5),
(0.5, -0.75),
(-0.5, -0.75),
(-0.75, -0.5),
(-0.75, 0.75),
(-0.5, 0.3),
(-0.25, 0.75)
]
GHOST_SIZE = 0.65
SCARED_COLOR = formatColor(1, 1, 1)
GHOST_VEC_COLORS = list(map(colorToVector, GHOST_COLORS))
PACMAN_COLOR = formatColor(255.0/255.0, 255.0/255.0, 61.0/255)
PACMAN_SCALE = 0.5
#pacman_speed = 0.25
# Food
FOOD_COLOR = formatColor(1, 1, 1)
FOOD_SIZE = 0.1
# Laser
LASER_COLOR = formatColor(1, 0, 0)
LASER_SIZE = 0.02
# Capsule graphics
CAPSULE_COLOR = formatColor(1, 1, 1)
CAPSULE_SIZE = 0.25
# Drawing walls
WALL_RADIUS = 0.15
class InfoPane:
def __init__(self, layout, gridSize):
self.gridSize = gridSize
self.width = (layout.width) * gridSize
self.base = (layout.height + 1) * gridSize
self.height = INFO_PANE_HEIGHT
self.fontSize = 24
self.textColor = PACMAN_COLOR
self.drawPane()
def toScreen(self, pos, y=None):
"""
Translates a point relative from the bottom left of the info pane.
"""
if y == None:
x, y = pos
else:
x = pos
x = self.gridSize + x # Margin
y = self.base + y
return x, y
def drawPane(self):
self.scoreText = text(self.toScreen(
0, 0), self.textColor, "SCORE: 0", "Times", self.fontSize, "bold")
def initializeGhostDistances(self, distances):
self.ghostDistanceText = []
size = 20
if self.width < 240:
size = 12
if self.width < 160:
size = 10
for i, d in enumerate(distances):
t = text(self.toScreen(self.width/2 + self.width/8 * i, 0),
GHOST_COLORS[i+1], d, "Times", size, "bold")
self.ghostDistanceText.append(t)
def updateScore(self, score):
changeText(self.scoreText, "SCORE: % 4d" % score)
def setTeam(self, isBlue):
text = "RED TEAM"
if isBlue:
text = "BLUE TEAM"
self.teamText = text(self.toScreen(
300, 0), self.textColor, text, "Times", self.fontSize, "bold")
def updateGhostDistances(self, distances):
if len(distances) == 0:
return
if 'ghostDistanceText' not in dir(self):
self.initializeGhostDistances(distances)
else:
for i, d in enumerate(distances):
changeText(self.ghostDistanceText[i], d)
def drawGhost(self):
pass
def drawPacman(self):
pass
def drawWarning(self):
pass
def clearIcon(self):
pass
def updateMessage(self, message):
pass
def clearMessage(self):
pass
class PacmanGraphics:
def __init__(self, zoom=1.0, frameTime=0.0, capture=False):
self.have_window = 0
self.currentGhostImages = {}
self.pacmanImage = None
self.zoom = zoom
self.gridSize = DEFAULT_GRID_SIZE * zoom
self.capture = capture
self.frameTime = frameTime
def checkNullDisplay(self):
return False
def initialize(self, state, isBlue=False):
self.isBlue = isBlue
self.startGraphics(state)
# self.drawDistributions(state)
self.distributionImages = None # Initialized lazily
self.drawStaticObjects(state)
self.drawAgentObjects(state)
# Information
self.previousState = state
def startGraphics(self, state):
self.layout = state.layout
layout = self.layout
self.width = layout.width
self.height = layout.height
self.make_window(self.width, self.height)
self.infoPane = InfoPane(layout, self.gridSize)
self.currentState = layout
def drawDistributions(self, state):
walls = state.layout.walls
dist = []
for x in range(walls.width):
distx = []
dist.append(distx)
for y in range(walls.height):
(screen_x, screen_y) = self.to_screen((x, y))
block = square((screen_x, screen_y),
0.5 * self.gridSize,
color=BACKGROUND_COLOR,
filled=1, behind=2)
distx.append(block)
self.distributionImages = dist
def drawStaticObjects(self, state):
layout = self.layout
self.drawWalls(layout.walls)
self.food = self.drawFood(layout.food)
self.capsules = self.drawCapsules(layout.capsules)
refresh()
def drawAgentObjects(self, state):
self.agentImages = [] # (agentState, image)
for index, agent in enumerate(state.agentStates):
if agent.isPacman:
image = self.drawPacman(agent, index)
self.agentImages.append((agent, image))
else:
image = self.drawGhost(agent, index)
self.agentImages.append((agent, image))
refresh()
def swapImages(self, agentIndex, newState):
"""
Changes an image from a ghost to a pacman or vis versa (for capture)
"""
prevState, prevImage = self.agentImages[agentIndex]
for item in prevImage:
remove_from_screen(item)
if newState.isPacman:
image = self.drawPacman(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image)
else:
image = self.drawGhost(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image)
refresh()
def update(self, newState):
agentIndex = newState._agentMoved
agentState = newState.agentStates[agentIndex]
if self.agentImages[agentIndex][0].isPacman != agentState.isPacman:
self.swapImages(agentIndex, agentState)
prevState, prevImage = self.agentImages[agentIndex]
if agentState.isPacman:
self.animatePacman(agentState, prevState, prevImage)
else:
self.moveGhost(agentState, agentIndex, prevState, prevImage)
self.agentImages[agentIndex] = (agentState, prevImage)
if newState._foodEaten != None:
self.removeFood(newState._foodEaten, self.food)
if newState._capsuleEaten != None:
self.removeCapsule(newState._capsuleEaten, self.capsules)
self.infoPane.updateScore(newState.score)
if 'ghostDistances' in dir(newState):
self.infoPane.updateGhostDistances(newState.ghostDistances)
def make_window(self, width, height):
grid_width = (width-1) * self.gridSize
grid_height = (height-1) * self.gridSize
screen_width = 2*self.gridSize + grid_width
screen_height = 2*self.gridSize + grid_height + INFO_PANE_HEIGHT
begin_graphics(screen_width,
screen_height,
BACKGROUND_COLOR,
"CS188 Pacman")
def drawPacman(self, pacman, index):
position = self.getPosition(pacman)
screen_point = self.to_screen(position)
endpoints = self.getEndpoints(self.getDirection(pacman))
width = PACMAN_OUTLINE_WIDTH
outlineColor = PACMAN_COLOR
fillColor = PACMAN_COLOR
if self.capture:
outlineColor = TEAM_COLORS[index % 2]
fillColor = GHOST_COLORS[index]
width = PACMAN_CAPTURE_OUTLINE_WIDTH
return [circle(screen_point, PACMAN_SCALE * self.gridSize,
fillColor=fillColor, outlineColor=outlineColor,
endpoints=endpoints,
width=width)]
def getEndpoints(self, direction, position=(0, 0)):
x, y = position
pos = x - int(x) + y - int(y)
width = 30 + 80 * math.sin(math.pi * pos)
delta = width / 2
if (direction == 'West'):
endpoints = (180+delta, 180-delta)
elif (direction == 'North'):
endpoints = (90+delta, 90-delta)
elif (direction == 'South'):
endpoints = (270+delta, 270-delta)
else:
endpoints = (0+delta, 0-delta)
return endpoints
def movePacman(self, position, direction, image):
screenPosition = self.to_screen(position)
endpoints = self.getEndpoints(direction, position)
r = PACMAN_SCALE * self.gridSize
moveCircle(image[0], screenPosition, r, endpoints)
refresh()
def animatePacman(self, pacman, prevPacman, image):
if self.frameTime < 0:
print('Press any key to step forward, "q" to play')
keys = wait_for_keys()
if 'q' in keys:
self.frameTime = 0.1
if self.frameTime > 0.01 or self.frameTime < 0:
start = time.time()
fx, fy = self.getPosition(prevPacman)
px, py = self.getPosition(pacman)
frames = 4.0
for i in range(1, int(frames) + 1):
pos = px*i/frames + fx * \
(frames-i)/frames, py*i/frames + fy*(frames-i)/frames
self.movePacman(pos, self.getDirection(pacman), image)
refresh()
sleep(abs(self.frameTime) / frames)
else:
self.movePacman(self.getPosition(pacman),
self.getDirection(pacman), image)
refresh()
def getGhostColor(self, ghost, ghostIndex):
if ghost.scaredTimer > 0:
return SCARED_COLOR
else:
return GHOST_COLORS[ghostIndex]
def drawGhost(self, ghost, agentIndex):
pos = self.getPosition(ghost)
dir = self.getDirection(ghost)
(screen_x, screen_y) = (self.to_screen(pos))
coords = []
for (x, y) in GHOST_SHAPE:
coords.append((x*self.gridSize*GHOST_SIZE + screen_x,
y*self.gridSize*GHOST_SIZE + screen_y))
colour = self.getGhostColor(ghost, agentIndex)
body = polygon(coords, colour, filled=1)
WHITE = formatColor(1.0, 1.0, 1.0)
BLACK = formatColor(0.0, 0.0, 0.0)
dx = 0
dy = 0
if dir == 'North':
dy = -0.2
if dir == 'South':
dy = 0.2
if dir == 'East':
dx = 0.2
if dir == 'West':
dx = -0.2
leftEye = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
rightEye = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
leftPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
rightPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
ghostImageParts = []
ghostImageParts.append(body)
ghostImageParts.append(leftEye)
ghostImageParts.append(rightEye)
ghostImageParts.append(leftPupil)
ghostImageParts.append(rightPupil)
return ghostImageParts
def moveEyes(self, pos, dir, eyes):
(screen_x, screen_y) = (self.to_screen(pos))
dx = 0
dy = 0
if dir == 'North':
dy = -0.2
if dir == 'South':
dy = 0.2
if dir == 'East':
dx = 0.2
if dir == 'West':
dx = -0.2
moveCircle(eyes[0], (screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
moveCircle(eyes[1], (screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
moveCircle(eyes[2], (screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
moveCircle(eyes[3], (screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y -
self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
def moveGhost(self, ghost, ghostIndex, prevGhost, ghostImageParts):
old_x, old_y = self.to_screen(self.getPosition(prevGhost))
new_x, new_y = self.to_screen(self.getPosition(ghost))
delta = new_x - old_x, new_y - old_y
for ghostImagePart in ghostImageParts:
move_by(ghostImagePart, delta)
refresh()
if ghost.scaredTimer > 0:
color = SCARED_COLOR
else:
color = GHOST_COLORS[ghostIndex]
edit(ghostImageParts[0], ('fill', color), ('outline', color))
self.moveEyes(self.getPosition(ghost),
self.getDirection(ghost), ghostImageParts[-4:])
refresh()
def getPosition(self, agentState):
if agentState.configuration == None:
return (-1000, -1000)
return agentState.getPosition()
def getDirection(self, agentState):
if agentState.configuration == None:
return Directions.STOP
return agentState.configuration.getDirection()
def finish(self):
end_graphics()
def to_screen(self, point):
(x, y) = point
#y = self.height - y
x = (x + 1)*self.gridSize
y = (self.height - y)*self.gridSize
return (x, y)
# Fixes some TK issue with off-center circles
def to_screen2(self, point):
(x, y) = point
#y = self.height - y
x = (x + 1)*self.gridSize
y = (self.height - y)*self.gridSize
return (x, y)
def drawWalls(self, wallMatrix):
wallColor = WALL_COLOR
for xNum, x in enumerate(wallMatrix):
if self.capture and (xNum * 2) < wallMatrix.width:
wallColor = TEAM_COLORS[0]
if self.capture and (xNum * 2) >= wallMatrix.width:
wallColor = TEAM_COLORS[1]
for yNum, cell in enumerate(x):
if cell: # There's a wall here
pos = (xNum, yNum)
screen = self.to_screen(pos)
screen2 = self.to_screen2(pos)
# draw each quadrant of the square based on adjacent walls
wIsWall = self.isWall(xNum-1, yNum, wallMatrix)
eIsWall = self.isWall(xNum+1, yNum, wallMatrix)
nIsWall = self.isWall(xNum, yNum+1, wallMatrix)
sIsWall = self.isWall(xNum, yNum-1, wallMatrix)
nwIsWall = self.isWall(xNum-1, yNum+1, wallMatrix)
swIsWall = self.isWall(xNum-1, yNum-1, wallMatrix)
neIsWall = self.isWall(xNum+1, yNum+1, wallMatrix)
seIsWall = self.isWall(xNum+1, yNum-1, wallMatrix)
# NE quadrant
if (not nIsWall) and (not eIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize,
wallColor, wallColor, (0, 91), 'arc')
if (nIsWall) and (not eIsWall):
# vertical line
line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen,
(self.gridSize*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
if (not nIsWall) and (eIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen,
(self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
if (nIsWall) and (eIsWall) and (not neIsWall):
# outer circle
circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)),
WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (180, 271), 'arc')
line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(-1)*WALL_RADIUS)),
add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)),
add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
# NW quadrant
if (not nIsWall) and (not wIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize,
wallColor, wallColor, (90, 181), 'arc')
if (nIsWall) and (not wIsWall):
# vertical line
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen,
(self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
if (not nIsWall) and (wIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen,
(self.gridSize*(-0.5)-1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
if (nIsWall) and (wIsWall) and (not nwIsWall):
# outer circle
circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)),
WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (270, 361), 'arc')
line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(-1)*WALL_RADIUS)),
add(screen, (self.gridSize*(-0.5), self.gridSize*(-1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)),
add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
# SE quadrant
if (not sIsWall) and (not eIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize,
wallColor, wallColor, (270, 361), 'arc')
if (sIsWall) and (not eIsWall):
# vertical line
line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen,
(self.gridSize*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
if (not sIsWall) and (eIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen,
(self.gridSize*0.5+1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
if (sIsWall) and (eIsWall) and (not seIsWall):
# outer circle
circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)),
WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (90, 181), 'arc')
line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(1)*WALL_RADIUS)),
add(screen, (self.gridSize*0.5, self.gridSize*(1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)),
add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
# SW quadrant
if (not sIsWall) and (not wIsWall):
# inner circle
circle(screen2, WALL_RADIUS * self.gridSize,
wallColor, wallColor, (180, 271), 'arc')
if (sIsWall) and (not wIsWall):
# vertical line
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen,
(self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
if (not sIsWall) and (wIsWall):
# horizontal line
line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen,
(self.gridSize*(-0.5)-1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
if (sIsWall) and (wIsWall) and (not swIsWall):
# outer circle
circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)),
WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (0, 91), 'arc')
line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(1)*WALL_RADIUS)),
add(screen, (self.gridSize*(-0.5), self.gridSize*(1)*WALL_RADIUS)), wallColor)
line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)),
add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
def isWall(self, x, y, walls):
if x < 0 or y < 0:
return False
if x >= walls.width or y >= walls.height:
return False
return walls[x][y]
def drawFood(self, foodMatrix):
foodImages = []
color = FOOD_COLOR
for xNum, x in enumerate(foodMatrix):
if self.capture and (xNum * 2) <= foodMatrix.width:
color = TEAM_COLORS[0]
if self.capture and (xNum * 2) > foodMatrix.width:
color = TEAM_COLORS[1]
imageRow = []
foodImages.append(imageRow)
for yNum, cell in enumerate(x):
if cell: # There's food here
screen = self.to_screen((xNum, yNum))
dot = circle(screen,
FOOD_SIZE * self.gridSize,
outlineColor=color, fillColor=color,
width=1)
imageRow.append(dot)
else:
imageRow.append(None)
return foodImages
def drawCapsules(self, capsules):
capsuleImages = {}
for capsule in capsules:
(screen_x, screen_y) = self.to_screen(capsule)
dot = circle((screen_x, screen_y),
CAPSULE_SIZE * self.gridSize,
outlineColor=CAPSULE_COLOR,
fillColor=CAPSULE_COLOR,
width=1)
capsuleImages[capsule] = dot
return capsuleImages
def removeFood(self, cell, foodImages):
x, y = cell
remove_from_screen(foodImages[x][y])
def removeCapsule(self, cell, capsuleImages):
x, y = cell
remove_from_screen(capsuleImages[(x, y)])
def drawExpandedCells(self, cells):
"""
Draws an overlay of expanded grid positions for search agents
"""
n = float(len(cells))
baseColor = [1.0, 0.0, 0.0]
self.clearExpandedCells()
self.expandedCells = []
for k, cell in enumerate(cells):
screenPos = self.to_screen(cell)
cellColor = formatColor(
*[(n-k) * c * .5 / n + .25 for c in baseColor])
block = square(screenPos,
0.5 * self.gridSize,
color=cellColor,
filled=1, behind=2)
self.expandedCells.append(block)
if self.frameTime < 0:
refresh()
def clearExpandedCells(self):
if 'expandedCells' in dir(self) and len(self.expandedCells) > 0:
for cell in self.expandedCells:
remove_from_screen(cell)
def updateDistributions(self, distributions):
"Draws an agent's belief distributions"
# copy all distributions so we don't change their state
distributions = [x.copy() for x in distributions]
if self.distributionImages == None:
self.drawDistributions(self.previousState)
for x in range(len(self.distributionImages)):
for y in range(len(self.distributionImages[0])):
image = self.distributionImages[x][y]
weights = [dist[(x, y)] for dist in distributions]
if sum(weights) != 0:
pass
# Fog of war
color = [0.0, 0.0, 0.0]
colors = GHOST_VEC_COLORS[1:] # With Pacman
if self.capture:
colors = GHOST_VEC_COLORS
for weight, gcolor in zip(weights, colors):
color = [min(1.0, c + 0.95 * g * weight ** .3)
for c, g in zip(color, gcolor)]
changeColor(image, formatColor(*color))
refresh()
class FirstPersonPacmanGraphics(PacmanGraphics):
def __init__(self, zoom=1.0, showGhosts=True, capture=False, frameTime=0):
PacmanGraphics.__init__(self, zoom, frameTime=frameTime)
self.showGhosts = showGhosts
self.capture = capture
def initialize(self, state, isBlue=False):
self.isBlue = isBlue
PacmanGraphics.startGraphics(self, state)
# Initialize distribution images
walls = state.layout.walls
dist = []
self.layout = state.layout
# Draw the rest
self.distributionImages = None # initialize lazily
self.drawStaticObjects(state)
self.drawAgentObjects(state)
# Information
self.previousState = state
def lookAhead(self, config, state):
if config.getDirection() == 'Stop':
return
else:
pass
# Draw relevant ghosts
allGhosts = state.getGhostStates()
visibleGhosts = state.getVisibleGhosts()
for i, ghost in enumerate(allGhosts):
if ghost in visibleGhosts:
self.drawGhost(ghost, i)
else:
self.currentGhostImages[i] = None
def getGhostColor(self, ghost, ghostIndex):
return GHOST_COLORS[ghostIndex]
def getPosition(self, ghostState):
if not self.showGhosts and not ghostState.isPacman and ghostState.getPosition()[1] > 1:
return (-1000, -1000)
else:
return PacmanGraphics.getPosition(self, ghostState)
def add(x, y):
return (x[0] + y[0], x[1] + y[1])
# Saving graphical output
# -----------------------
# Note: to make an animated gif from this postscript output, try the command:
# convert -delay 7 -loop 1 -compress lzw -layers optimize frame* out.gif
# convert is part of imagemagick (freeware)
SAVE_POSTSCRIPT = False
POSTSCRIPT_OUTPUT_DIR = 'frames'
FRAME_NUMBER = 0
import os
def saveFrame():
"Saves the current graphical output as a postscript file"
global SAVE_POSTSCRIPT, FRAME_NUMBER, POSTSCRIPT_OUTPUT_DIR
if not SAVE_POSTSCRIPT:
return
if not os.path.exists(POSTSCRIPT_OUTPUT_DIR):
os.mkdir(POSTSCRIPT_OUTPUT_DIR)
name = os.path.join(POSTSCRIPT_OUTPUT_DIR, 'frame_%08d.ps' % FRAME_NUMBER)
FRAME_NUMBER += 1
writePostscript(name) # writes the current canvas
homework_2/graphicsUtils.py
# graphicsUtils.py
# ----------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
import sys
import math
import random
import string
import time
import types
import tkinter
import os.path
_Windows = sys.platform == 'win32' # True if on Win95/98/NT
_root_window = None # The root window for graphics output
_canvas = None # The canvas which holds graphics
_canvas_xs = None # Size of canvas object
_canvas_ys = None
_canvas_x = None # Current position on canvas
_canvas_y = None
_canvas_col = None # Current colour (set to black below)
_canvas_tsize = 12
_canvas_tserifs = 0
def formatColor(r, g, b):
return '#%02x%02x%02x' % (int(r * 255), int(g * 255), int(b * 255))
def colorToVector(color):
return [int(x, 16) / 256.0 for x in [color[1:3], color[3:5], color[5:7]]]
if _Windows:
_canvas_tfonts = ['times new roman', 'lucida console']
else:
_canvas_tfonts = ['times', 'lucidasans-24']
pass # XXX need defaults here
def sleep(secs):
global _root_window
if _root_window == None:
time.sleep(secs)
else:
_root_window.update_idletasks()
_root_window.after(int(1000 * secs), _root_window.quit)
_root_window.mainloop()
def begin_graphics(width=640, height=480, color=formatColor(0, 0, 0), title=None):
global _root_window, _canvas, _canvas_x, _canvas_y, _canvas_xs, _canvas_ys, _bg_color
# Check for duplicate call
if _root_window is not None:
# Lose the window.
_root_window.destroy()
# Save the canvas size parameters
_canvas_xs, _canvas_ys = width - 1, height - 1
_canvas_x, _canvas_y = 0, _canvas_ys
_bg_color = color
# Create the root window
_root_window = tkinter.Tk()
_root_window.protocol('WM_DELETE_WINDOW', _destroy_window)
_root_window.title(title or 'Graphics Window')
_root_window.resizable(0, 0)
# Create the canvas object
try:
_canvas = tkinter.Canvas(_root_window, width=width, height=height)
_canvas.pack()
draw_background()
_canvas.update()
except:
_root_window = None
raise
# Bind to key-down and key-up events
_root_window.bind("", _keypress)
_root_window.bind("", _keyrelease)
_root_window.bind("", _clear_keys)
_root_window.bind("", _clear_keys)
_root_window.bind("", _leftclick)
_root_window.bind("", _rightclick)
_root_window.bind("", _rightclick)
_root_window.bind("", _ctrl_leftclick)
_clear_keys()
_leftclick_loc = None
_rightclick_loc = None
_ctrl_leftclick_loc = None
def _leftclick(event):
global _leftclick_loc
_leftclick_loc = (event.x, event.y)
def _rightclick(event):
global _rightclick_loc
_rightclick_loc = (event.x, event.y)
def _ctrl_leftclick(event):
global _ctrl_leftclick_loc
_ctrl_leftclick_loc = (event.x, event.y)
def wait_for_click():
while True:
global _leftclick_loc
global _rightclick_loc
global _ctrl_leftclick_loc
if _leftclick_loc != None:
val = _leftclick_loc
_leftclick_loc = None
return val, 'left'
if _rightclick_loc != None:
val = _rightclick_loc
_rightclick_loc = None
return val, 'right'
if _ctrl_leftclick_loc != None:
val = _ctrl_leftclick_loc
_ctrl_leftclick_loc = None
return val, 'ctrl_left'
sleep(0.05)
def draw_background():
corners = [(0, 0), (0, _canvas_ys),
(_canvas_xs, _canvas_ys), (_canvas_xs, 0)]
polygon(corners, _bg_color, fillColor=_bg_color,
filled=True, smoothed=False)
def _destroy_window(event=None):
sys.exit(0)
# global _root_window
# _root_window.destroy()
# _root_window = None
# print "DESTROY"
def end_graphics():
global _root_window, _canvas, _mouse_enabled
try:
try:
sleep(1)
if _root_window != None:
_root_window.destroy()
except SystemExit as e:
print('Ending graphics raised an exception:', e)
finally:
_root_window = None
_canvas = None
_mouse_enabled = 0
_clear_keys()
def clear_screen(background=None):
global _canvas_x, _canvas_y
_canvas.delete('all')
draw_background()
_canvas_x, _canvas_y = 0, _canvas_ys
def polygon(coords, outlineColor, fillColor=None, filled=1, smoothed=1, behind=0, width=1):
c = []
for coord in coords:
c.append(coord[0])
c.append(coord[1])
if fillColor == None:
fillColor = outlineColor
if filled == 0:
fillColor = ""
poly = _canvas.create_polygon(
c, outline=outlineColor, fill=fillColor, smooth=smoothed, width=width)
if behind > 0:
_canvas.tag_lower(poly, behind) # Higher should be more visible
return poly
def square(pos, r, color, filled=1, behind=0):
x, y = pos
coords = [(x - r, y - r), (x + r, y - r), (x + r, y + r), (x - r, y + r)]
return polygon(coords, color, color, filled, 0, behind=behind)
def circle(pos, r, outlineColor, fillColor, endpoints=None, style='pieslice', width=2):
x, y = pos
x0, x1 = x - r - 1, x + r
y0, y1 = y - r - 1, y + r
if endpoints == None:
e = [0, 359]
else:
e = list(endpoints)
while e[0] > e[1]:
e[1] = e[1] + 360
return _canvas.create_arc(x0, y0, x1, y1, outline=outlineColor, fill=fillColor,
extent=e[1] - e[0], start=e[0], style=style, width=width)
def image(pos, file="../../blueghost.gif"):
x, y = pos
# img = PhotoImage(file=file)
return _canvas.create_image(x, y, image=tkinter.PhotoImage(file=file), anchor=tkinter.NW)
def refresh():
_canvas.update_idletasks()
def moveCircle(id, pos, r, endpoints=None):
global _canvas_x, _canvas_y
x, y = pos
# x0, x1 = x - r, x + r + 1
# y0, y1 = y - r, y + r + 1
x0, x1 = x - r - 1, x + r
y0, y1 = y - r - 1, y + r
if endpoints == None:
e = [0, 359]
else:
e = list(endpoints)
while e[0] > e[1]:
e[1] = e[1] + 360
if os.path.isfile('flag'):
edit(id, ('extent', e[1] - e[0]))
else:
edit(id, ('start', e[0]), ('extent', e[1] - e[0]))
move_to(id, x0, y0)
def edit(id, *args):
_canvas.itemconfigure(id, **dict(args))
def text(pos, color, contents, font='Helvetica', size=12, style='normal', anchor="nw"):
global _canvas_x, _canvas_y
x, y = pos
font = (font, str(size), style)
return _canvas.create_text(x, y, fill=color, text=contents, font=font, anchor=anchor)
def changeText(id, newText, font=None, size=12, style='normal'):
_canvas.itemconfigure(id, text=newText)
if font != None:
_canvas.itemconfigure(id, font=(font, '-%d' % size, style))
def changeColor(id, newColor):
_canvas.itemconfigure(id, fill=newColor)
def line(here, there, color=formatColor(0, 0, 0), width=2):
x0, y0 = here[0], here[1]
x1, y1 = there[0], there[1]
return _canvas.create_line(x0, y0, x1, y1, fill=color, width=width)
##############################################################################
### Keypress handling ########################################################
##############################################################################
# We bind to key-down and key-up events.
_keysdown = {}
_keyswaiting = {}
# This holds an unprocessed key release. We delay key releases by up to
# one call to keys_pressed() to get round a problem with auto repeat.
_got_release = None
def _keypress(event):
global _got_release
# remap_arrows(event)
_keysdown[event.keysym] = 1
_keyswaiting[event.keysym] = 1
# print event.char, event.keycode
_got_release = None
def _keyrelease(event):
global _got_release
# remap_arrows(event)
try:
del _keysdown[event.keysym]
except:
pass
_got_release = 1
def remap_arrows(event):
# TURN ARROW PRESSES INTO LETTERS (SHOULD BE IN KEYBOARD AGENT)
if event.char in ['a', 's', 'd', 'w']:
return
if event.keycode in [37, 101]: # LEFT ARROW (win / x)
event.char = 'a'
if event.keycode in [38, 99]: # UP ARROW
event.char = 'w'
if event.keycode in [39, 102]: # RIGHT ARROW
event.char = 'd'
if event.keycode in [40, 104]: # DOWN ARROW
event.char = 's'
def _clear_keys(event=None):
global _keysdown, _got_release, _keyswaiting
_keysdown = {}
_keyswaiting = {}
_got_release = None
def keys_pressed(d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT):
d_o_e(d_w)
if _got_release:
d_o_e(d_w)
return list(_keysdown.keys())
def keys_waiting():
global _keyswaiting
keys = list(_keyswaiting.keys())
_keyswaiting = {}
return keys
# Block for a list of keys...
def wait_for_keys():
keys = []
while keys == []:
keys = keys_pressed()
sleep(0.05)
return keys
def remove_from_screen(x,
d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT):
_canvas.delete(x)
d_o_e(d_w)
def _adjust_coords(coord_list, x, y):
for i in range(0, len(coord_list), 2):
coord_list[i] = coord_list[i] + x
coord_list[i + 1] = coord_list[i + 1] + y
return coord_list
def move_to(object, x, y=None,
d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT):
if y is None:
try:
x, y = x
except:
raise Exception('incomprehensible coordinates')
horiz = True
newCoords = []
current_x, current_y = _canvas.coords(object)[0:2] # first point
for coord in _canvas.coords(object):
if horiz:
inc = x - current_x
else:
inc = y - current_y
horiz = not horiz
newCoords.append(coord + inc)
_canvas.coords(object, *newCoords)
d_o_e(d_w)
def move_by(object, x, y=None,
d_o_e=lambda arg: _root_window.dooneevent(arg),
d_w=tkinter._tkinter.DONT_WAIT, lift=False):
if y is None:
try:
x, y = x
except:
raise Exception('incomprehensible coordinates')
horiz = True
newCoords = []
for coord in _canvas.coords(object):
if horiz:
inc = x
else:
inc = y
horiz = not horiz
newCoords.append(coord + inc)
_canvas.coords(object, *newCoords)
d_o_e(d_w)
if lift:
_canvas.tag_raise(object)
def writePostscript(filename):
"Writes the current canvas to a postscript file."
psfile = file(filename, 'w')
psfile.write(_canvas.postscript(pageanchor='sw',
y='0.c',
x='0.c'))
psfile.close()
ghost_shape = [
(0, - 0.5),
(0.25, - 0.75),
(0.5, - 0.5),
(0.75, - 0.75),
(0.75, 0.5),
(0.5, 0.75),
(- 0.5, 0.75),
(- 0.75, 0.5),
(- 0.75, - 0.75),
(- 0.5, - 0.5),
(- 0.25, - 0.75)
]
if __name__ == '__main__':
begin_graphics()
clear_screen()
ghost_shape = [(x * 10 + 20, y * 10 + 20) for x, y in ghost_shape]
g = polygon(ghost_shape, formatColor(1, 1, 1))
move_to(g, (50, 50))
circle((150, 150), 20, formatColor(0.7, 0.3, 0.0), endpoints=[15, - 15])
sleep(2)
homework_2/keyboardAgents.py
# keyboardAgents.py
# -----------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from game import Agent
from game import Directions
import random
class KeyboardAgent(Agent):
"""
An agent controlled by the keyboard.
"""
# NOTE: Arrow keys also work.
WEST_KEY = 'a'
EAST_KEY = 'd'
NORTH_KEY = 'w'
SOUTH_KEY = 's'
STOP_KEY = 'q'
def __init__(self, index=0):
self.lastMove = Directions.STOP
self.index = index
self.keys = []
def getAction(self, state):
from graphicsUtils import keys_waiting
from graphicsUtils import keys_pressed
keys = keys_waiting() + keys_pressed()
if keys != []:
self.keys = keys
legal = state.getLegalActions(self.index)
move = self.getMove(legal)
if move == Directions.STOP:
# Try to move in the same direction as before
if self.lastMove in legal:
move = self.lastMove
if (self.STOP_KEY in self.keys) and Directions.STOP in legal:
move = Directions.STOP
if move not in legal:
move = random.choice(legal)
self.lastMove = move
return move
def getMove(self, legal):
move = Directions.STOP
if (self.WEST_KEY in self.keys or 'Left' in self.keys) and Directions.WEST in legal:
move = Directions.WEST
if (self.EAST_KEY in self.keys or 'Right' in self.keys) and Directions.EAST in legal:
move = Directions.EAST
if (self.NORTH_KEY in self.keys or 'Up' in self.keys) and Directions.NORTH in legal:
move = Directions.NORTH
if (self.SOUTH_KEY in self.keys or 'Down' in self.keys) and Directions.SOUTH in legal:
move = Directions.SOUTH
return move
class KeyboardAgent2(KeyboardAgent):
"""
A second agent controlled by the keyboard.
"""
# NOTE: Arrow keys also work.
WEST_KEY = 'j'
EAST_KEY = "l"
NORTH_KEY = 'i'
SOUTH_KEY = 'k'
STOP_KEY = 'u'
def getMove(self, legal):
move = Directions.STOP
if (self.WEST_KEY in self.keys) and Directions.WEST in legal:
move = Directions.WEST
if (self.EAST_KEY in self.keys) and Directions.EAST in legal:
move = Directions.EAST
if (self.NORTH_KEY in self.keys) and Directions.NORTH in legal:
move = Directions.NORTH
if (self.SOUTH_KEY in self.keys) and Directions.SOUTH in legal:
move = Directions.SOUTH
return move
homework_2/layout.py
# layout.py
# ---------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from util import manhattanDistance
from game import Grid
import os
import random
from functools import reduce
VISIBILITY_MATRIX_CACHE = {}
class Layout:
"""
A Layout manages the static information about the game board.
"""
def __init__(self, layoutText):
self.width = len(layoutText[0])
self.height = len(layoutText)
self.walls = Grid(self.width, self.height, False)
self.food = Grid(self.width, self.height, False)
self.capsules = []
self.agentPositions = []
self.numGhosts = 0
self.processLayoutText(layoutText)
self.layoutText = layoutText
self.totalFood = len(self.food.asList())
# self.initializeVisibilityMatrix()
def getNumGhosts(self):
return self.numGhosts
def initializeVisibilityMatrix(self):
global VISIBILITY_MATRIX_CACHE
if reduce(str.__add__, self.layoutText) not in VISIBILITY_MATRIX_CACHE:
from game import Directions
vecs = [(-0.5, 0), (0.5, 0), (0, -0.5), (0, 0.5)]
dirs = [Directions.NORTH, Directions.SOUTH,
Directions.WEST, Directions.EAST]
vis = Grid(self.width, self.height, {Directions.NORTH: set(), Directions.SOUTH: set(
), Directions.EAST: set(), Directions.WEST: set(), Directions.STOP: set()})
for x in range(self.width):
for y in range(self.height):
if self.walls[x][y] == False:
for vec, direction in zip(vecs, dirs):
dx, dy = vec
nextx, nexty = x + dx, y + dy
while (nextx + nexty) != int(nextx) + int(nexty) or not self.walls[int(nextx)][int(nexty)]:
vis[x][y][direction].add((nextx, nexty))
nextx, nexty = x + dx, y + dy
self.visibility = vis
VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)] = vis
else:
self.visibility = VISIBILITY_MATRIX_CACHE[reduce(
str.__add__, self.layoutText)]
def isWall(self, pos):
x, col = pos
return self.walls[x][col]
def getRandomLegalPosition(self):
x = random.choice(list(range(self.width)))
y = random.choice(list(range(self.height)))
while self.isWall((x, y)):
x = random.choice(list(range(self.width)))
y = random.choice(list(range(self.height)))
return (x, y)
def getRandomCorner(self):
poses = [(1, 1), (1, self.height - 2), (self.width - 2, 1),
(self.width - 2, self.height - 2)]
return random.choice(poses)
def getFurthestCorner(self, pacPos):
poses = [(1, 1), (1, self.height - 2), (self.width - 2, 1),
(self.width - 2, self.height - 2)]
dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
return pos
def isVisibleFrom(self, ghostPos, pacPos, pacDirection):
row, col = [int(x) for x in pacPos]
return ghostPos in self.visibility[row][col][pacDirection]
def __str__(self):
return "\n".join(self.layoutText)
def deepCopy(self):
return Layout(self.layoutText[:])
def processLayoutText(self, layoutText):
"""
Coordinates are flipped from the input format to the (x,y) convention here
The shape of the maze. Each character
represents a different type of object.
% - Wall
. - Food
o - Capsule
G - Ghost
P - Pacman
Other characters are ignored.
"""
maxY = self.height - 1
for y in range(self.height):
for x in range(self.width):
layoutChar = layoutText[maxY - y][x]
self.processLayoutChar(x, y, layoutChar)
self.agentPositions.sort()
self.agentPositions = [(i == 0, pos) for i, pos in self.agentPositions]
def processLayoutChar(self, x, y, layoutChar):
if layoutChar == '%':
self.walls[x][y] = True
elif layoutChar == '.':
self.food[x][y] = True
elif layoutChar == 'o':
self.capsules.append((x, y))
elif layoutChar == 'P':
self.agentPositions.append((0, (x, y)))
elif layoutChar in ['G']:
self.agentPositions.append((1, (x, y)))
self.numGhosts += 1
elif layoutChar in ['1', '2', '3', '4']:
self.agentPositions.append((int(layoutChar), (x, y)))
self.numGhosts += 1
def getLayout(name, back=2):
if name.endswith('.lay'):
layout = tryToLoad('layouts/' + name)
if layout == None:
layout = tryToLoad(name)
else:
layout = tryToLoad('layouts/' + name + '.lay')
if layout == None:
layout = tryToLoad(name + '.lay')
if layout == None and back >= 0:
curdir = os.path.abspath('.')
os.chdir('..')
layout = getLayout(name, back - 1)
os.chdir(curdir)
return layout
def tryToLoad(fullname):
if(not os.path.exists(fullname)):
return None
f = open(fullname)
try:
return Layout([line.strip() for line in f])
finally:
f.close()
homework_2/layouts/capsuleClassic.lay
%%%%%%%%%%%%%%%%%%%
%G. G ....%
%.% % %%%%%% %.%%.%
%.%o% % o% %.o%.%
%.%%%.% %%% %..%.%
%..... P %..%G%
%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/contestClassic.lay
%%%%%%%%%%%%%%%%%%%%
%o...%........%...o%
%.%%.%.%%..%%.%.%%.%
%...... G GG%......%
%.%.%%.%% %%%.%%.%.%
%.%....% ooo%.%..%.%
%.%.%%.% %% %.%.%%.%
%o%......P....%....%
%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/mediumClassic.lay
%%%%%%%%%%%%%%%%%%%%
%o...%........%....%
%.%%.%.%%%%%%.%.%%.%
%.%..............%.%
%.%.%%.%% %%.%%.%.%
%......%G G%......%
%.%.%%.%%%%%%.%%.%.%
%.%..............%.%
%.%%.%.%%%%%%.%.%%.%
%....%...P....%...o%
%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/minimaxClassic.lay
%%%%%%%%%
%.P G%
% %.%G%%%
%G %%%
%%%%%%%%%
homework_2/layouts/openClassic.lay
%%%%%%%%%%%%%%%%%%%%%%%%%
%.. P .... .... %
%.. ... ... ... ... %
%.. ... ... ... ... %
%.. .... .... G %
%.. ... ... ... ... %
%.. ... ... ... ... %
%.. .... .... o%
%%%%%%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/originalClassic.lay
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%............%%............%
%.%%%%.%%%%%.%%.%%%%%.%%%%.%
%o%%%%.%%%%%.%%.%%%%%.%%%%o%
%.%%%%.%%%%%.%%.%%%%%.%%%%.%
%..........................%
%.%%%%.%%.%%%%%%%%.%%.%%%%.%
%.%%%%.%%.%%%%%%%%.%%.%%%%.%
%......%%....%%....%%......%
%%%%%%.%%%%% %% %%%%%.%%%%%%
%%%%%%.%%%%% %% %%%%%.%%%%%%
%%%%%%.% %.%%%%%%
%%%%%%.% %%%% %%%% %.%%%%%%
% . %G GG G% . %
%%%%%%.% %%%%%%%%%% %.%%%%%%
%%%%%%.% %.%%%%%%
%%%%%%.% %%%%%%%%%% %.%%%%%%
%............%%............%
%.%%%%.%%%%%.%%.%%%%%.%%%%.%
%.%%%%.%%%%%.%%.%%%%%.%%%%.%
%o..%%....... .......%%..o%
%%%.%%.%%.%%%%%%%%.%%.%%.%%%
%%%.%%.%%.%%%%%%%%.%%.%%.%%%
%......%%....%%....%%......%
%.%%%%%%%%%%.%%.%%%%%%%%%%.%
%.............P............%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/powerClassic.lay
%%%%%%%%%%%%%%%%%%%%
%o....o%GGGG%o....o%
%..%...%% %%...%..%
%.%o.%........%.o%.%
%.o%.%.%%%%%%.%.%o.%
%........P.........%
%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/smallClassic.lay
%%%%%%%%%%%%%%%%%%%%
%......%G G%......%
%.%%...%% %%...%%.%
%.%o.%........%.o%.%
%.%%.%.%%%%%%.%.%%.%
%........P.........%
%%%%%%%%%%%%%%%%%%%%
homework_2/layouts/testClassic.lay
%%%%%
% . %
%.G.%
% . %
%. .%
% %
% .%
% %
%P .%
%%%%%
homework_2/layouts/trappedClassic.lay
%%%%%%%%
% P G%
%G%%%%%%
%.... %
%%%%%%%%
homework_2/layouts/trickyClassic.lay
%%%%%%%%%%%%%%%%%%%%
%o...%........%...o%
%.%%.%.%%..%%.%.%%.%
%.%.....%..%.....%.%
%.%.%%.%% %%.%%.%.%
%...... GGGG%.%....%
%.%....%%%%%%.%..%.%
%.%....% oo%.%..%.%
%.%....% %%%%.%..%.%
%.%...........%..%.%
%.%%.%.%%%%%%.%.%%.%
%o...%...P....%...o%
%%%%%%%%%%%%%%%%%%%%
homework_2/multiAgents.py
# multiAgents.py
# --------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from util import manhattanDistance
from game import Directions
import random, util
from game import Agent
class ReflexAgent(Agent):
"""
A reflex agent chooses an action at each choice point by examining
its alternatives via a state evaluation function.
The code below is provided as a guide. You are welcome to change
it in any way you see fit, so long as you don't touch our method
headers.
"""
def getAction(self, gameState):
"""
You do not need to change this method, but you're welcome to.
getAction chooses among the best options according to the evaluation function.
Just like in the previous project, getAction takes a GameState and returns
some Directions.X for some X in the set {NORTH, SOUTH, WEST, EAST, STOP}
"""
# Collect legal moves and successor states
legalMoves = gameState.getLegalActions()
# Choose one of the best actions
scores = [self.evaluationFunction(gameState, action) for action in legalMoves]
bestScore = max(scores)
bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]
chosenIndex = random.choice(bestIndices) # Pick randomly among the best
"Add more of your code here if you want to"
return legalMoves[chosenIndex]
def evaluationFunction(self, currentGameState, action):
"""
Design a better evaluation function here.
The evaluation function takes in the current and proposed successor
GameStates (pacman.py) and returns a number, where higher numbers are better.
The code below extracts some useful information from the state, like the
remaining food (newFood) and Pacman position after moving (newPos).
newScaredTimes holds the number of moves that each ghost will remain
scared because of Pacman having eaten a power pellet.
Print out these variables to see what you're getting, then combine them
to create a masterful evaluation function.
"""
# Useful information you can extract from a GameState (pacman.py)
successorGameState = currentGameState.generatePacmanSuccessor(action)
newPos = successorGameState.getPacmanPosition()
newFood = successorGameState.getFood()
newGhostStates = successorGameState.getGhostStates()
newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]
"*** YOUR CODE HERE ***"
return successorGameState.getScore()
def scoreEvaluationFunction(currentGameState):
"""
This default evaluation function just returns the score of the state.
The score is the same one displayed in the Pacman GUI.
This evaluation function is meant for use with adversarial search agents
(not reflex agents).
"""
return currentGameState.getScore()
class MultiAgentSearchAgent(Agent):
"""
This class provides some common elements to all of your
multi-agent searchers. Any methods defined here will be available
to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent.
You *do not* need to make any changes here, but you can if you want to
add functionality to all your adversarial search agents. Please do not
remove anything, however.
Note: this is an abstract class: one that should not be instantiated. It's
only partially specified, and designed to be extended. Agent (game.py)
is another abstract class.
"""
def __init__(self, evalFn = 'scoreEvaluationFunction', depth = '2'):
self.index = 0 # Pacman is always agent index 0
self.evaluationFunction = util.lookup(evalFn, globals())
self.depth = int(depth)
class MinimaxAgent(MultiAgentSearchAgent):
"""
Your minimax agent (question 2)
"""
def getAction(self, gameState):
"""
Returns the minimax action from the current gameState using self.depth
and self.evaluationFunction.
Here are some method calls that might be useful when implementing minimax.
gameState.getLegalActions(agentIndex):
Returns a list of legal actions for an agent
agentIndex=0 means Pacman, ghosts are >= 1
gameState.generateSuccessor(agentIndex, action):
Returns the successor game state after an agent takes an action
gameState.getNumAgents():
Returns the total number of agents in the game
gameState.isWin():
Returns whether or not the game state is a winning state
gameState.isLose():
Returns whether or not the game state is a losing state
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
class AlphaBetaAgent(MultiAgentSearchAgent):
"""
Your minimax agent with alpha-beta pruning (question 3)
"""
def getAction(self, gameState):
"""
Returns the minimax action using self.depth and self.evaluationFunction
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
class ExpectimaxAgent(MultiAgentSearchAgent):
"""
Your expectimax agent (question 4)
"""
def getAction(self, gameState):
"""
Returns the expectimax action using self.depth and self.evaluationFunction
All ghosts should be modeled as choosing uniformly at random from their
legal moves.
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
def betterEvaluationFunction(currentGameState):
"""
Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable
evaluation function (question 5).
DESCRIPTION:
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
# Abbreviation
better = betterEvaluationFunction
homework_2/multiagentTestClasses.py
# multiagentTestClasses.py
# ------------------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
# A minimax tree which interfaces like gameState
# state.getNumAgents()
# state.isWin()
# state.isLose()
# state.generateSuccessor(agentIndex, action)
# state.getScore()
# used by multiAgents.scoreEvaluationFunction, which is the default
#
import testClasses
import json
from collections import defaultdict
from pprint import PrettyPrinter
pp = PrettyPrinter()
from game import Agent
from pacman import GameState
from ghostAgents import RandomGhost, DirectionalGhost
import random
import math
import traceback
import sys
import os
import layout
import pacman
import autograder
# import grading
VERBOSE = False
class MultiagentTreeState(object):
def __init__(self, problem, state):
self.problem = problem
self.state = state
def generateSuccessor(self, agentIndex, action):
if VERBOSE:
print("generateSuccessor(%s, %s, %s) -> %s" % (self.state, agentIndex,
action, self.problem.stateToSuccessorMap[self.state][action]))
successor = self.problem.stateToSuccessorMap[self.state][action]
self.problem.generatedStates.add(successor)
return MultiagentTreeState(self.problem, successor)
def getScore(self):
if VERBOSE:
print("getScore(%s) -> %s" %
(self.state, self.problem.evaluation[self.state]))
if self.state not in self.problem.evaluation:
raise Exception(
'getScore() called on non-terminal state or before maximum depth achieved.')
return float(self.problem.evaluation[self.state])
def getLegalActions(self, agentIndex=0):
if VERBOSE:
print("getLegalActions(%s) -> %s" %
(self.state, self.problem.stateToActions[self.state]))
# if len(self.problem.stateToActions[self.state]) == 0:
# print "WARNING: getLegalActions called on leaf state %s" % (self.state,)
return list(self.problem.stateToActions[self.state])
def isWin(self):
if VERBOSE:
print("isWin(%s) -> %s" %
(self.state, self.state in self.problem.winStates))
return self.state in self.problem.winStates
def isLose(self):
if VERBOSE:
print("isLose(%s) -> %s" %
(self.state, self.state in self.problem.loseStates))
return self.state in self.problem.loseStates
def getNumAgents(self):
if VERBOSE:
print("getNumAgents(%s) -> %s" %
(self.state, self.problem.numAgents))
return self.problem.numAgents
class MultiagentTreeProblem(object):
def __init__(self, numAgents, startState, winStates, loseStates, successors, evaluation):
self.startState = MultiagentTreeState(self, startState)
self.numAgents = numAgents
self.winStates = winStates
self.loseStates = loseStates
self.evaluation = evaluation
self.successors = successors
self.reset()
self.stateToSuccessorMap = defaultdict(dict)
self.stateToActions = defaultdict(list)
for state, action, nextState in successors:
self.stateToActions[state].append(action)
self.stateToSuccessorMap[state][action] = nextState
def reset(self):
self.generatedStates = set([self.startState.state])
def parseTreeProblem(testDict):
numAgents = int(testDict["num_agents"])
startState = testDict["start_state"]
winStates = set(testDict["win_states"].split(" "))
loseStates = set(testDict["lose_states"].split(" "))
successors = []
evaluation = {}
for line in testDict["evaluation"].split('\n'):
tokens = line.split()
if len(tokens) == 2:
state, value = tokens
evaluation[state] = float(value)
else:
raise Exception("[parseTree] Bad evaluation line: |%s|" % (line,))
for line in testDict["successors"].split('\n'):
tokens = line.split()
if len(tokens) == 3:
state, action, nextState = tokens
successors.append((state, action, nextState))
else:
raise Exception("[parseTree] Bad successor line: |%s|" % (line,))
return MultiagentTreeProblem(numAgents, startState, winStates, loseStates, successors, evaluation)
def run(lay, layName, pac, ghosts, disp, nGames=1, name='games'):
"""
Runs a few games and outputs their statistics.
"""
starttime = time.time()
print('*** Running %s on' % name, layName, '%d time(s).' % nGames)
games = pacman.runGames(lay, pac, ghosts, disp,
nGames, False, catchExceptions=True, timeout=120)
print('*** Finished running %s on' % name, layName,
'after %d seconds.' % (time.time() - starttime))
stats = {'time': time.time() - starttime, 'wins': [g.state.isWin() for g in games].count(True), 'games': games, 'scores': [g.state.getScore() for g in games],
'timeouts': [g.agentTimeout for g in games].count(True), 'crashes': [g.agentCrashed for g in games].count(True)}
print('*** Won %d out of %d games. Average score: %f ***' %
(stats['wins'], len(games), sum(stats['scores']) * 1.0 / len(games)))
return stats
class GradingAgent(Agent):
def __init__(self, seed, studentAgent, optimalActions, altDepthActions, partialPlyBugActions):
# save student agent and actions of refernce agents
self.studentAgent = studentAgent
self.optimalActions = optimalActions
self.altDepthActions = altDepthActions
self.partialPlyBugActions = partialPlyBugActions
# create fields for storing specific wrong actions
self.suboptimalMoves = []
self.wrongStatesExplored = -1
# boolean vectors represent types of implementation the student could have
self.actionsConsistentWithOptimal = [
True for i in range(len(optimalActions[0]))]
self.actionsConsistentWithAlternativeDepth = [
True for i in range(len(altDepthActions[0]))]
self.actionsConsistentWithPartialPlyBug = [
True for i in range(len(partialPlyBugActions[0]))]
# keep track of elapsed moves
self.stepCount = 0
self.seed = seed
def registerInitialState(self, state):
if 'registerInitialState' in dir(self.studentAgent):
self.studentAgent.registerInitialState(state)
random.seed(self.seed)
def getAction(self, state):
GameState.getAndResetExplored()
studentAction = (self.studentAgent.getAction(state),
len(GameState.getAndResetExplored()))
optimalActions = self.optimalActions[self.stepCount]
altDepthActions = self.altDepthActions[self.stepCount]
partialPlyBugActions = self.partialPlyBugActions[self.stepCount]
studentOptimalAction = False
curRightStatesExplored = False
for i in range(len(optimalActions)):
if studentAction[0] in optimalActions[i][0]:
studentOptimalAction = True
else:
self.actionsConsistentWithOptimal[i] = False
if studentAction[1] == int(optimalActions[i][1]):
curRightStatesExplored = True
if not curRightStatesExplored and self.wrongStatesExplored < 0:
self.wrongStatesExplored = 1
for i in range(len(altDepthActions)):
if studentAction[0] not in altDepthActions[i]:
self.actionsConsistentWithAlternativeDepth[i] = False
for i in range(len(partialPlyBugActions)):
if studentAction[0] not in partialPlyBugActions[i]:
self.actionsConsistentWithPartialPlyBug[i] = False
if not studentOptimalAction:
self.suboptimalMoves.append(
(state, studentAction[0], optimalActions[0][0][0]))
self.stepCount += 1
random.seed(self.seed + self.stepCount)
return optimalActions[0][0][0]
def getSuboptimalMoves(self):
return self.suboptimalMoves
def getWrongStatesExplored(self):
return self.wrongStatesExplored
def checkFailure(self):
"""
Return +n if have n suboptimal moves.
Return -1 if have only off by one depth moves.
Return 0 otherwise.
"""
if self.wrongStatesExplored > 0:
return -3
if self.actionsConsistentWithOptimal.count(True) > 0:
return 0
elif self.actionsConsistentWithPartialPlyBug.count(True) > 0:
return -2
elif self.actionsConsistentWithAlternativeDepth.count(True) > 0:
return -1
else:
return len(self.suboptimalMoves)
class PolyAgent(Agent):
def __init__(self, seed, multiAgents, ourPacOptions, depth):
# prepare our pacman agents
solutionAgents, alternativeDepthAgents, partialPlyBugAgents = self.construct_our_pacs(
multiAgents, ourPacOptions)
for p in solutionAgents:
p.depth = depth
for p in partialPlyBugAgents:
p.depth = depth
for p in alternativeDepthAgents[:2]:
p.depth = max(1, depth - 1)
for p in alternativeDepthAgents[2:]:
p.depth = depth + 1
self.solutionAgents = solutionAgents
self.alternativeDepthAgents = alternativeDepthAgents
self.partialPlyBugAgents = partialPlyBugAgents
# prepare fields for storing the results
self.optimalActionLists = []
self.alternativeDepthLists = []
self.partialPlyBugLists = []
self.seed = seed
self.stepCount = 0
def select(self, list, indices):
"""
Return a sublist of elements given by indices in list.
"""
return [list[i] for i in indices]
def construct_our_pacs(self, multiAgents, keyword_dict):
pacs_without_stop = [multiAgents.StaffMultiAgentSearchAgent(
**keyword_dict) for i in range(3)]
keyword_dict['keepStop'] = 'True'
pacs_with_stop = [multiAgents.StaffMultiAgentSearchAgent(
**keyword_dict) for i in range(3)]
keyword_dict['usePartialPlyBug'] = 'True'
partial_ply_bug_pacs = [
multiAgents.StaffMultiAgentSearchAgent(**keyword_dict)]
keyword_dict['keepStop'] = 'False'
partial_ply_bug_pacs = partial_ply_bug_pacs + \
[multiAgents.StaffMultiAgentSearchAgent(**keyword_dict)]
for pac in pacs_with_stop + pacs_without_stop + partial_ply_bug_pacs:
pac.verbose = False
ourpac = [pacs_with_stop[0], pacs_without_stop[0]]
alternative_depth_pacs = self.select(
pacs_with_stop + pacs_without_stop, [1, 4, 2, 5])
return (ourpac, alternative_depth_pacs, partial_ply_bug_pacs)
def registerInitialState(self, state):
for agent in self.solutionAgents + self.alternativeDepthAgents:
if 'registerInitialState' in dir(agent):
agent.registerInitialState(state)
random.seed(self.seed)
def getAction(self, state):
# survey agents
GameState.getAndResetExplored()
optimalActionLists = []
for agent in self.solutionAgents:
optimalActionLists.append((agent.getBestPacmanActions(
state)[0], len(GameState.getAndResetExplored())))
alternativeDepthLists = [agent.getBestPacmanActions(
state)[0] for agent in self.alternativeDepthAgents]
partialPlyBugLists = [agent.getBestPacmanActions(
state)[0] for agent in self.partialPlyBugAgents]
# record responses
self.optimalActionLists.append(optimalActionLists)
self.alternativeDepthLists.append(alternativeDepthLists)
self.partialPlyBugLists.append(partialPlyBugLists)
self.stepCount += 1
random.seed(self.seed + self.stepCount)
return optimalActionLists[0][0][0]
def getTraces(self):
# return traces from individual agents
return (self.optimalActionLists, self.alternativeDepthLists, self.partialPlyBugLists)
class PacmanGameTreeTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(PacmanGameTreeTest, self).__init__(question, testDict)
self.seed = int(self.testDict['seed'])
self.alg = self.testDict['alg']
self.layout_text = self.testDict['layout']
self.layout_name = self.testDict['layoutName']
self.depth = int(self.testDict['depth'])
self.max_points = int(self.testDict['max_points'])
def execute(self, grades, moduleDict, solutionDict):
# load student code and staff code solutions
multiAgents = moduleDict['multiAgents']
studentAgent = getattr(multiAgents, self.alg)(depth=self.depth)
allActions = [json.loads(x)
for x in solutionDict['optimalActions'].split('\n')]
altDepthActions = [json.loads(
x) for x in solutionDict['altDepthActions'].split('\n')]
partialPlyBugActions = [json.loads(
x) for x in solutionDict['partialPlyBugActions'].split('\n')]
# set up game state and play a game
random.seed(self.seed)
lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
pac = GradingAgent(self.seed, studentAgent, allActions,
altDepthActions, partialPlyBugActions)
# check return codes and assign grades
disp = self.question.getDisplay()
stats = run(lay, self.layout_name, pac, [DirectionalGhost(
i + 1) for i in range(2)], disp, name=self.alg)
if stats['timeouts'] > 0:
self.addMessage('Agent timed out on smallClassic. No credit')
return self.testFail(grades)
if stats['crashes'] > 0:
self.addMessage('Agent crashed on smallClassic. No credit')
return self.testFail(grades)
code = pac.checkFailure()
if code == 0:
return self.testPass(grades)
elif code == -3:
if pac.getWrongStatesExplored() >= 0:
self.addMessage('Bug: Wrong number of states expanded.')
return self.testFail(grades)
else:
return self.testPass(grades)
elif code == -2:
self.addMessage('Bug: Partial Ply Bug')
return self.testFail(grades)
elif code == -1:
self.addMessage('Bug: Search depth off by 1')
return self.testFail(grades)
elif code > 0:
moves = pac.getSuboptimalMoves()
state, studentMove, optMove = random.choice(moves)
self.addMessage('Bug: Suboptimal moves')
self.addMessage('State:%s\nStudent Move:%s\nOptimal Move:%s' % (
state, studentMove, optMove))
return self.testFail(grades)
def writeList(self, handle, name, list):
handle.write('%s: """\n' % name)
for l in list:
handle.write('%s\n' % json.dumps(l))
handle.write('"""\n')
def writeSolution(self, moduleDict, filePath):
# load module, set seed, create ghosts and macman, run game
multiAgents = moduleDict['multiAgents']
random.seed(self.seed)
lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
if self.alg == 'ExpectimaxAgent':
ourPacOptions = {'expectimax': 'True'}
elif self.alg == 'AlphaBetaAgent':
ourPacOptions = {'alphabeta': 'True'}
else:
ourPacOptions = {}
pac = PolyAgent(self.seed, multiAgents, ourPacOptions, self.depth)
disp = self.question.getDisplay()
run(lay, self.layout_name, pac, [DirectionalGhost(
i + 1) for i in range(2)], disp, name=self.alg)
(optimalActions, altDepthActions, partialPlyBugActions) = pac.getTraces()
# recover traces and record to file
handle = open(filePath, 'w')
self.writeList(handle, 'optimalActions', optimalActions)
self.writeList(handle, 'altDepthActions', altDepthActions)
self.writeList(handle, 'partialPlyBugActions', partialPlyBugActions)
handle.close()
class GraphGameTreeTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(GraphGameTreeTest, self).__init__(question, testDict)
self.problem = parseTreeProblem(testDict)
self.alg = self.testDict['alg']
self.diagram = self.testDict['diagram'].split('\n')
self.depth = int(self.testDict['depth'])
def solveProblem(self, multiAgents):
self.problem.reset()
studentAgent = getattr(multiAgents, self.alg)(depth=self.depth)
action = studentAgent.getAction(self.problem.startState)
generated = self.problem.generatedStates
return action, " ".join([str(s) for s in sorted(generated)])
def addDiagram(self):
self.addMessage('Tree:')
for line in self.diagram:
self.addMessage(line)
def execute(self, grades, moduleDict, solutionDict):
multiAgents = moduleDict['multiAgents']
goldAction = solutionDict['action']
goldGenerated = solutionDict['generated']
action, generated = self.solveProblem(multiAgents)
fail = False
if action != goldAction:
self.addMessage('Incorrect move for depth=%s' % (self.depth,))
self.addMessage(
' Student move: %s\n Optimal move: %s' % (action, goldAction))
fail = True
if generated != goldGenerated:
self.addMessage(
'Incorrect generated nodes for depth=%s' % (self.depth,))
self.addMessage(' Student generated nodes: %s\n Correct generated nodes: %s' % (
generated, goldGenerated))
fail = True
if fail:
self.addDiagram()
return self.testFail(grades)
else:
return self.testPass(grades)
def writeSolution(self, moduleDict, filePath):
multiAgents = moduleDict['multiAgents']
action, generated = self.solveProblem(multiAgents)
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n' % self.path)
handle.write('action: "%s"\n' % (action,))
handle.write('generated: "%s"\n' % (generated,))
return True
import time
from util import TimeoutFunction
class EvalAgentTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(EvalAgentTest, self).__init__(question, testDict)
self.layoutName = testDict['layoutName']
self.agentName = testDict['agentName']
self.ghosts = eval(testDict['ghosts'])
self.maxTime = int(testDict['maxTime'])
self.seed = int(testDict['randomSeed'])
self.numGames = int(testDict['numGames'])
self.scoreMinimum = int(
testDict['scoreMinimum']) if 'scoreMinimum' in testDict else None
self.nonTimeoutMinimum = int(
testDict['nonTimeoutMinimum']) if 'nonTimeoutMinimum' in testDict else None
self.winsMinimum = int(
testDict['winsMinimum']) if 'winsMinimum' in testDict else None
self.scoreThresholds = [int(s) for s in testDict.get(
'scoreThresholds', '').split()]
self.nonTimeoutThresholds = [int(s) for s in testDict.get(
'nonTimeoutThresholds', '').split()]
self.winsThresholds = [int(s) for s in testDict.get(
'winsThresholds', '').split()]
self.maxPoints = sum([len(t) for t in [
self.scoreThresholds, self.nonTimeoutThresholds, self.winsThresholds]])
self.agentArgs = testDict.get('agentArgs', '')
def execute(self, grades, moduleDict, solutionDict):
startTime = time.time()
agentType = getattr(moduleDict['multiAgents'], self.agentName)
agentOpts = pacman.parseAgentArgs(
self.agentArgs) if self.agentArgs != '' else {}
agent = agentType(**agentOpts)
lay = layout.getLayout(self.layoutName, 3)
disp = self.question.getDisplay()
random.seed(self.seed)
games = pacman.runGames(lay, agent, self.ghosts, disp, self.numGames,
False, catchExceptions=True, timeout=self.maxTime)
totalTime = time.time() - startTime
stats = {'time': totalTime, 'wins': [g.state.isWin() for g in games].count(True),
'games': games, 'scores': [g.state.getScore() for g in games],
'timeouts': [g.agentTimeout for g in games].count(True), 'crashes': [g.agentCrashed for g in games].count(True)}
averageScore = sum(stats['scores']) / float(len(stats['scores']))
nonTimeouts = self.numGames - stats['timeouts']
wins = stats['wins']
def gradeThreshold(value, minimum, thresholds, name):
points = 0
passed = (minimum == None) or (value >= minimum)
if passed:
for t in thresholds:
if value >= t:
points += 1
return (passed, points, value, minimum, thresholds, name)
results = [gradeThreshold(averageScore, self.scoreMinimum, self.scoreThresholds, "average score"),
gradeThreshold(nonTimeouts, self.nonTimeoutMinimum,
self.nonTimeoutThresholds, "games not timed out"),
gradeThreshold(wins, self.winsMinimum, self.winsThresholds, "wins")]
totalPoints = 0
for passed, points, value, minimum, thresholds, name in results:
if minimum == None and len(thresholds) == 0:
continue
# print passed, points, value, minimum, thresholds, name
totalPoints += points
if not passed:
assert points == 0
self.addMessage(
"%s %s (fail: below minimum value %s)" % (value, name, minimum))
else:
self.addMessage("%s %s (%s of %s points)" %
(value, name, points, len(thresholds)))
if minimum != None:
self.addMessage(" Grading scheme:")
self.addMessage(" < %s: fail" % (minimum,))
if len(thresholds) == 0 or minimum != thresholds[0]:
self.addMessage(" >= %s: 0 points" % (minimum,))
for idx, threshold in enumerate(thresholds):
self.addMessage(" >= %s: %s points" %
(threshold, idx+1))
elif len(thresholds) > 0:
self.addMessage(" Grading scheme:")
self.addMessage(" < %s: 0 points" % (thresholds[0],))
for idx, threshold in enumerate(thresholds):
self.addMessage(" >= %s: %s points" %
(threshold, idx+1))
if any([not passed for passed, _, _, _, _, _ in results]):
totalPoints = 0
return self.testPartial(grades, totalPoints, self.maxPoints)
def writeSolution(self, moduleDict, filePath):
handle = open(filePath, 'w')
handle.write('# This is the solution file for %s.\n' % self.path)
handle.write('# File intentionally blank.\n')
handle.close()
return True
homework_2/pacman.py
# pacman.py
# ---------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
"""
Pacman.py holds the logic for the classic pacman game along with the main
code to run a game. This file is divided into three sections:
(i) Your interface to the pacman world:
Pacman is a complex environment. You probably don't want to
read through all of the code we wrote to make the game runs
correctly. This section contains the parts of the code
that you will need to understand in order to complete the
project. There is also some code in game.py that you should
understand.
(ii) The hidden secrets of pacman:
This section contains all of the logic code that the pacman
environment uses to decide who can move where, who dies when
things collide, etc. You shouldn't need to read this section
of code, but you can if you want.
(iii) Framework to start a game:
The final section contains the code for reading the command
you use to set up the game, then starting up a new game, along with
linking in all the external parts (agent functions, graphics).
Check this section out to see all the options available to you.
To play your first game, type 'python pacman.py' from the command line.
The keys are 'a', 's', 'd', and 'w' to move (or arrow keys). Have fun!
"""
from game import GameStateData
from game import Game
from game import Directions
from game import Actions
from util import nearestPoint
from util import manhattanDistance
import util
import layout
import sys
import types
import time
import random
import os
###################################################
# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
###################################################
class GameState:
"""
A GameState specifies the full game state, including the food, capsules,
agent configurations and score changes.
GameStates are used by the Game object to capture the actual state of the game and
can be used by agents to reason about the game.
Much of the information in a GameState is stored in a GameStateData object. We
strongly suggest that you access that data via the accessor methods below rather
than referring to the GameStateData object directly.
Note that in classic Pacman, Pacman is always agent 0.
"""
####################################################
# Accessor methods: use these to access state data #
####################################################
# static variable keeps track of which states have had getLegalActions called
explored = set()
def getAndResetExplored():
tmp = GameState.explored.copy()
GameState.explored = set()
return tmp
getAndResetExplored = staticmethod(getAndResetExplored)
def getLegalActions(self, agentIndex=0):
"""
Returns the legal actions for the agent specified.
"""
# GameState.explored.add(self)
if self.isWin() or self.isLose():
return []
if agentIndex == 0: # Pacman is moving
return PacmanRules.getLegalActions(self)
else:
return GhostRules.getLegalActions(self, agentIndex)
def generateSuccessor(self, agentIndex, action):
"""
Returns the successor state after the specified agent takes the action.
"""
# Check that successors exist
if self.isWin() or self.isLose():
raise Exception('Can\'t generate a successor of a terminal state.')
# Copy current state
state = GameState(self)
# Let agent's logic deal with its action's effects on the board
if agentIndex == 0: # Pacman is moving
state.data._eaten = [False for i in range(state.getNumAgents())]
PacmanRules.applyAction(state, action)
else: # A ghost is moving
GhostRules.applyAction(state, action, agentIndex)
# Time passes
if agentIndex == 0:
state.data.scoreChange += -TIME_PENALTY # Penalty for waiting around
else:
GhostRules.decrementTimer(state.data.agentStates[agentIndex])
# Resolve multi-agent effects
GhostRules.checkDeath(state, agentIndex)
# Book keeping
state.data._agentMoved = agentIndex
state.data.score += state.data.scoreChange
GameState.explored.add(self)
GameState.explored.add(state)
return state
def getLegalPacmanActions(self):
return self.getLegalActions(0)
def generatePacmanSuccessor(self, action):
"""
Generates the successor state after the specified pacman move
"""
return self.generateSuccessor(0, action)
def getPacmanState(self):
"""
Returns an AgentState object for pacman (in game.py)
state.pos gives the current position
state.direction gives the travel vector
"""
return self.data.agentStates[0].copy()
def getPacmanPosition(self):
return self.data.agentStates[0].getPosition()
def getGhostStates(self):
return self.data.agentStates[1:]
def getGhostState(self, agentIndex):
if agentIndex == 0 or agentIndex >= self.getNumAgents():
raise Exception("Invalid index passed to getGhostState")
return self.data.agentStates[agentIndex]
def getGhostPosition(self, agentIndex):
if agentIndex == 0:
raise Exception("Pacman's index passed to getGhostPosition")
return self.data.agentStates[agentIndex].getPosition()
def getGhostPositions(self):
return [s.getPosition() for s in self.getGhostStates()]
def getNumAgents(self):
return len(self.data.agentStates)
def getScore(self):
return float(self.data.score)
def getCapsules(self):
"""
Returns a list of positions (x,y) of the remaining capsules.
"""
return self.data.capsules
def getNumFood(self):
return self.data.food.count()
def getFood(self):
"""
Returns a Grid of boolean food indicator variables.
Grids can be accessed via list notation, so to check
if there is food at (x,y), just call
currentFood = state.getFood()
if currentFood[x][y] == True: ...
"""
return self.data.food
def getWalls(self):
"""
Returns a Grid of boolean wall indicator variables.
Grids can be accessed via list notation, so to check
if there is a wall at (x,y), just call
walls = state.getWalls()
if walls[x][y] == True: ...
"""
return self.data.layout.walls
def hasFood(self, x, y):
return self.data.food[x][y]
def hasWall(self, x, y):
return self.data.layout.walls[x][y]
def isLose(self):
return self.data._lose
def isWin(self):
return self.data._win
#############################################
# Helper methods: #
# You shouldn't need to call these directly #
#############################################
def __init__(self, prevState=None):
"""
Generates a new state by copying information from its predecessor.
"""
if prevState != None: # Initial state
self.data = GameStateData(prevState.data)
else:
self.data = GameStateData()
def deepCopy(self):
state = GameState(self)
state.data = self.data.deepCopy()
return state
def __eq__(self, other):
"""
Allows two states to be compared.
"""
return hasattr(other, 'data') and self.data == other.data
def __hash__(self):
"""
Allows states to be keys of dictionaries.
"""
return hash(self.data)
def __str__(self):
return str(self.data)
def initialize(self, layout, numGhostAgents=1000):
"""
Creates an initial game state from a layout array (see layout.py).
"""
self.data.initialize(layout, numGhostAgents)
############################################################################
# THE HIDDEN SECRETS OF PACMAN #
# #
# You shouldn't need to look through the code in this section of the file. #
############################################################################
SCARED_TIME = 40 # Moves ghosts are scared
COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
TIME_PENALTY = 1 # Number of points lost each round
class ClassicGameRules:
"""
These game rules manage the control flow of a game, deciding when
and how the game starts and ends.
"""
def __init__(self, timeout=30):
self.timeout = timeout
def newGame(self, layout, pacmanAgent, ghostAgents, display, quiet=False, catchExceptions=False):
agents = [pacmanAgent] + ghostAgents[:layout.getNumGhosts()]
initState = GameState()
initState.initialize(layout, len(ghostAgents))
game = Game(agents, display, self, catchExceptions=catchExceptions)
game.state = initState
self.initialState = initState.deepCopy()
self.quiet = quiet
return game
def process(self, state, game):
"""
Checks to see whether it is time to end the game.
"""
if state.isWin():
self.win(state, game)
if state.isLose():
self.lose(state, game)
def win(self, state, game):
if not self.quiet:
print("Pacman emerges victorious! Score: %d" % state.data.score)
game.gameOver = True
def lose(self, state, game):
if not self.quiet:
print("Pacman died! Score: %d" % state.data.score)
game.gameOver = True
def getProgress(self, game):
return float(game.state.getNumFood()) / self.initialState.getNumFood()
def agentCrash(self, game, agentIndex):
if agentIndex == 0:
print("Pacman crashed")
else:
print("A ghost crashed")
def getMaxTotalTime(self, agentIndex):
return self.timeout
def getMaxStartupTime(self, agentIndex):
return self.timeout
def getMoveWarningTime(self, agentIndex):
return self.timeout
def getMoveTimeout(self, agentIndex):
return self.timeout
def getMaxTimeWarnings(self, agentIndex):
return 0
class PacmanRules:
"""
These functions govern how pacman interacts with his environment under
the classic game rules.
"""
PACMAN_SPEED = 1
def getLegalActions(state):
"""
Returns a list of possible actions.
"""
return Actions.getPossibleActions(state.getPacmanState().configuration, state.data.layout.walls)
getLegalActions = staticmethod(getLegalActions)
def applyAction(state, action):
"""
Edits the state to reflect the results of the action.
"""
legal = PacmanRules.getLegalActions(state)
if action not in legal:
raise Exception("Illegal action " + str(action))
pacmanState = state.data.agentStates[0]
# Update Configuration
vector = Actions.directionToVector(action, PacmanRules.PACMAN_SPEED)
pacmanState.configuration = pacmanState.configuration.generateSuccessor(
vector)
# Eat
next = pacmanState.configuration.getPosition()
nearest = nearestPoint(next)
if manhattanDistance(nearest, next) <= 0.5:
# Remove food
PacmanRules.consume(nearest, state)
applyAction = staticmethod(applyAction)
def consume(position, state):
x, y = position
# Eat food
if state.data.food[x][y]:
state.data.scoreChange += 10
state.data.food = state.data.food.copy()
state.data.food[x][y] = False
state.data._foodEaten = position
# TODO: cache numFood?
numFood = state.getNumFood()
if numFood == 0 and not state.data._lose:
state.data.scoreChange += 500
state.data._win = True
# Eat capsule
if(position in state.getCapsules()):
state.data.capsules.remove(position)
state.data._capsuleEaten = position
# Reset all ghosts' scared timers
for index in range(1, len(state.data.agentStates)):
state.data.agentStates[index].scaredTimer = SCARED_TIME
consume = staticmethod(consume)
class GhostRules:
"""
These functions dictate how ghosts interact with their environment.
"""
GHOST_SPEED = 1.0
def getLegalActions(state, ghostIndex):
"""
Ghosts cannot stop, and cannot turn around unless they
reach a dead end, but can turn 90 degrees at intersections.
"""
conf = state.getGhostState(ghostIndex).configuration
possibleActions = Actions.getPossibleActions(
conf, state.data.layout.walls)
reverse = Actions.reverseDirection(conf.direction)
if Directions.STOP in possibleActions:
possibleActions.remove(Directions.STOP)
if reverse in possibleActions and len(possibleActions) > 1:
possibleActions.remove(reverse)
return possibleActions
getLegalActions = staticmethod(getLegalActions)
def applyAction(state, action, ghostIndex):
legal = GhostRules.getLegalActions(state, ghostIndex)
if action not in legal:
raise Exception("Illegal ghost action " + str(action))
ghostState = state.data.agentStates[ghostIndex]
speed = GhostRules.GHOST_SPEED
if ghostState.scaredTimer > 0:
speed /= 2.0
vector = Actions.directionToVector(action, speed)
ghostState.configuration = ghostState.configuration.generateSuccessor(
vector)
applyAction = staticmethod(applyAction)
def decrementTimer(ghostState):
timer = ghostState.scaredTimer
if timer == 1:
ghostState.configuration.pos = nearestPoint(
ghostState.configuration.pos)
ghostState.scaredTimer = max(0, timer - 1)
decrementTimer = staticmethod(decrementTimer)
def checkDeath(state, agentIndex):
pacmanPosition = state.getPacmanPosition()
if agentIndex == 0: # Pacman just moved; Anyone can kill him
for index in range(1, len(state.data.agentStates)):
ghostState = state.data.agentStates[index]
ghostPosition = ghostState.configuration.getPosition()
if GhostRules.canKill(pacmanPosition, ghostPosition):
GhostRules.collide(state, ghostState, index)
else:
ghostState = state.data.agentStates[agentIndex]
ghostPosition = ghostState.configuration.getPosition()
if GhostRules.canKill(pacmanPosition, ghostPosition):
GhostRules.collide(state, ghostState, agentIndex)
checkDeath = staticmethod(checkDeath)
def collide(state, ghostState, agentIndex):
if ghostState.scaredTimer > 0:
state.data.scoreChange += 200
GhostRules.placeGhost(state, ghostState)
ghostState.scaredTimer = 0
# Added for first-person
state.data._eaten[agentIndex] = True
else:
if not state.data._win:
state.data.scoreChange -= 500
state.data._lose = True
collide = staticmethod(collide)
def canKill(pacmanPosition, ghostPosition):
return manhattanDistance(ghostPosition, pacmanPosition) <= COLLISION_TOLERANCE
canKill = staticmethod(canKill)
def placeGhost(state, ghostState):
ghostState.configuration = ghostState.start
placeGhost = staticmethod(placeGhost)
#############################
# FRAMEWORK TO START A GAME #
#############################
def default(str):
return str + ' [Default: %default]'
def parseAgentArgs(str):
if str == None:
return {}
pieces = str.split(',')
opts = {}
for p in pieces:
if '=' in p:
key, val = p.split('=')
else:
key, val = p, 1
opts[key] = val
return opts
def readCommand(argv):
"""
Processes the command used to run pacman from the command line.
"""
from optparse import OptionParser
usageStr = """
USAGE: python pacman.py
EXAMPLES: (1) python pacman.py
- starts an interactive game
(2) python pacman.py --layout smallClassic --zoom 2
OR python pacman.py -l smallClassic -z 2
- starts an interactive game on a smaller board, zoomed in
"""
parser = OptionParser(usageStr)
parser.add_option('-n', '--numGames', dest='numGames', type='int',
help=default('the number of GAMES to play'), metavar='GAMES', default=1)
parser.add_option('-l', '--layout', dest='layout',
help=default(
'the LAYOUT_FILE from which to load the map layout'),
metavar='LAYOUT_FILE', default='mediumClassic')
parser.add_option('-p', '--pacman', dest='pacman',
help=default(
'the agent TYPE in the pacmanAgents module to use'),
metavar='TYPE', default='KeyboardAgent')
parser.add_option('-t', '--textGraphics', action='store_true', dest='textGraphics',
help='Display output as text only', default=False)
parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
help='Generate minimal output and no graphics', default=False)
parser.add_option('-g', '--ghosts', dest='ghost',
help=default(
'the ghost agent TYPE in the ghostAgents module to use'),
metavar='TYPE', default='RandomGhost')
parser.add_option('-k', '--numghosts', type='int', dest='numGhosts',
help=default('The maximum number of ghosts to use'), default=4)
parser.add_option('-z', '--zoom', type='float', dest='zoom',
help=default('Zoom the size of the graphics window'), default=1.0)
parser.add_option('-f', '--fixRandomSeed', action='store_true', dest='fixRandomSeed',
help='Fixes the random seed to always play the same game', default=False)
parser.add_option('-r', '--recordActions', action='store_true', dest='record',
help='Writes game histories to a file (named by the time they were played)', default=False)
parser.add_option('--replay', dest='gameToReplay',
help='A recorded game file (pickle) to replay', default=None)
parser.add_option('-a', '--agentArgs', dest='agentArgs',
help='Comma separated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
parser.add_option('-x', '--numTraining', dest='numTraining', type='int',
help=default('How many episodes are training (suppresses output)'), default=0)
parser.add_option('--frameTime', dest='frameTime', type='float',
help=default('Time to delay between frames; <0 means keyboard'), default=0.1)
parser.add_option('-c', '--catchExceptions', action='store_true', dest='catchExceptions',
help='Turns on exception handling and timeouts during games', default=False)
parser.add_option('--timeout', dest='timeout', type='int',
help=default('Maximum length of time an agent can spend computing in a single game'), default=30)
options, otherjunk = parser.parse_args(argv)
if len(otherjunk) != 0:
raise Exception('Command line input not understood: ' + str(otherjunk))
args = dict()
# Fix the random seed
if options.fixRandomSeed:
random.seed('cs188')
# Choose a layout
args['layout'] = layout.getLayout(options.layout)
if args['layout'] == None:
raise Exception("The layout " + options.layout + " cannot be found")
# Choose a Pacman agent
noKeyboard = options.gameToReplay == None and (
options.textGraphics or options.quietGraphics)
pacmanType = loadAgent(options.pacman, noKeyboard)
agentOpts = parseAgentArgs(options.agentArgs)
if options.numTraining > 0:
args['numTraining'] = options.numTraining
if 'numTraining' not in agentOpts:
agentOpts['numTraining'] = options.numTraining
pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
args['pacman'] = pacman
# Don't display training games
if 'numTrain' in agentOpts:
options.numQuiet = int(agentOpts['numTrain'])
options.numIgnore = int(agentOpts['numTrain'])
# Choose a ghost agent
ghostType = loadAgent(options.ghost, noKeyboard)
args['ghosts'] = [ghostType(i+1) for i in range(options.numGhosts)]
# Choose a display format
if options.quietGraphics:
import textDisplay
args['display'] = textDisplay.NullGraphics()
elif options.textGraphics:
import textDisplay
textDisplay.SLEEP_TIME = options.frameTime
args['display'] = textDisplay.PacmanGraphics()
else:
import graphicsDisplay
args['display'] = graphicsDisplay.PacmanGraphics(
options.zoom, frameTime=options.frameTime)
args['numGames'] = options.numGames
args['record'] = options.record
args['catchExceptions'] = options.catchExceptions
args['timeout'] = options.timeout
# Special case: recorded games don't use the runGames method or args structure
if options.gameToReplay != None:
print('Replaying recorded game %s.' % options.gameToReplay)
import pickle
f = open(options.gameToReplay)
try:
recorded = pickle.load(f)
finally:
f.close()
recorded['display'] = args['display']
replayGame(**recorded)
sys.exit(0)
return args
def loadAgent(pacman, nographics):
# Looks through all pythonPath Directories for the right module,
pythonPathStr = os.path.expandvars("$PYTHONPATH")
if pythonPathStr.find(';') == -1:
pythonPathDirs = pythonPathStr.split(':')
else:
pythonPathDirs = pythonPathStr.split(';')
pythonPathDirs.append('.')
for moduleDir in pythonPathDirs:
if not os.path.isdir(moduleDir):
continue
moduleNames = [f for f in os.listdir(
moduleDir) if f.endswith('gents.py')]
for modulename in moduleNames:
try:
module = __import__(modulename[:-3])
except ImportError:
continue
if pacman in dir(module):
if nographics and modulename == 'keyboardAgents.py':
raise Exception(
'Using the keyboard requires graphics (not text display)')
return getattr(module, pacman)
raise Exception('The agent ' + pacman +
' is not specified in any *Agents.py.')
def replayGame(layout, actions, display):
import pacmanAgents
import ghostAgents
rules = ClassicGameRules()
agents = [pacmanAgents.GreedyAgent()] + [ghostAgents.RandomGhost(i+1)
for i in range(layout.getNumGhosts())]
game = rules.newGame(layout, agents[0], agents[1:], display)
state = game.state
display.initialize(state.data)
for action in actions:
# Execute the action
state = state.generateSuccessor(*action)
# Change the display
display.update(state.data)
# Allow for game specific conditions (winning, losing, etc.)
rules.process(state, game)
display.finish()
def runGames(layout, pacman, ghosts, display, numGames, record, numTraining=0, catchExceptions=False, timeout=30):
import __main__
__main__.__dict__['_display'] = display
rules = ClassicGameRules(timeout)
games = []
for i in range(numGames):
beQuiet = i < numTraining
if beQuiet:
# Suppress output and graphics
import textDisplay
gameDisplay = textDisplay.NullGraphics()
rules.quiet = True
else:
gameDisplay = display
rules.quiet = False
game = rules.newGame(layout, pacman, ghosts,
gameDisplay, beQuiet, catchExceptions)
game.run()
if not beQuiet:
games.append(game)
if record:
import time
import pickle
fname = ('recorded-game-%d' % (i + 1)) + \
'-'.join([str(t) for t in time.localtime()[1:6]])
f = file(fname, 'w')
components = {'layout': layout, 'actions': game.moveHistory}
pickle.dump(components, f)
f.close()
if (numGames-numTraining) > 0:
scores = [game.state.getScore() for game in games]
wins = [game.state.isWin() for game in games]
winRate = wins.count(True) / float(len(wins))
print('Average Score:', sum(scores) / float(len(scores)))
print('Scores: ', ', '.join([str(score) for score in scores]))
print('Win Rate: %d/%d (%.2f)' %
(wins.count(True), len(wins), winRate))
print('Record: ', ', '.join(
[['Loss', 'Win'][int(w)] for w in wins]))
return games
if __name__ == '__main__':
"""
The main function called when pacman.py is run
from the command line:
> python pacman.py
See the usage string for more details.
> python pacman.py --help
"""
args = readCommand(sys.argv[1:]) # Get game components based on input
runGames(**args)
# import cProfile
# cProfile.run("runGames( **args )")
pass
homework_2/pacmanAgents.py
# pacmanAgents.py
# ---------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from pacman import Directions
from game import Agent
import random
import game
import util
class LeftTurnAgent(game.Agent):
"An agent that turns left at every opportunity"
def getAction(self, state):
legal = state.getLegalPacmanActions()
current = state.getPacmanState().configuration.direction
if current == Directions.STOP:
current = Directions.NORTH
left = Directions.LEFT[current]
if left in legal:
return left
if current in legal:
return current
if Directions.RIGHT[current] in legal:
return Directions.RIGHT[current]
if Directions.LEFT[left] in legal:
return Directions.LEFT[left]
return Directions.STOP
class GreedyAgent(Agent):
def __init__(self, evalFn="scoreEvaluation"):
self.evaluationFunction = util.lookup(evalFn, globals())
assert self.evaluationFunction != None
def getAction(self, state):
# Generate candidate actions
legal = state.getLegalPacmanActions()
if Directions.STOP in legal:
legal.remove(Directions.STOP)
successors = [(state.generateSuccessor(0, action), action)
for action in legal]
scored = [(self.evaluationFunction(state), action)
for state, action in successors]
bestScore = max(scored)[0]
bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
return random.choice(bestActions)
def scoreEvaluation(state):
return state.getScore()
homework_2/projectParams.py
# projectParams.py
# ----------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
STUDENT_CODE_DEFAULT = 'multiAgents.py'
PROJECT_TEST_CLASSES = 'multiagentTestClasses.py'
PROJECT_NAME = 'Project 2: Multiagent search'
BONUS_PIC = False
homework_2/submission_autograder.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from codecs import open
import os, ssl
if (not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None)):
ssl._create_default_https_context = ssl._create_unverified_context
"""
CS 188 Local Submission Autograder
Written by the CS 188 Staff
==============================================================================
_____ _ _
/ ____| | | |
| (___ | |_ ___ _ __ | |
\___ \| __/ _ \| '_ \| |
____) | || (_) | |_) |_|
|_____/ \__\___/| .__/(_)
| |
|_|
Modifying or tampering with this file is a violation of course policy.
If you're having trouble running the autograder, please contact the staff.
==============================================================================
"""
import bz2, base64
exec(bz2.decompress(base64.b64decode('QlpoOTFBWSZTWcngzx4AO57fgHkQfv///3////7////7YB1cEk+5twc46nOaALplk1ixNhG4Btg0AAANATBVKW2ORoA6NUKVJlTKLroCNAO466AA4JIhATEGmmk0CaegJiJtJtNU8AhpBp4po02mU0MNMpo0CE0JqT09Ep7SmhhMjZGo0yPUaDQaDRiBoaHA0aMQaNMmEGIDEYmjRo0AaaaAAAAJNFIkETUynkagw1DIA0yPUZDRpoaA0YRpkGIcDRoxBo0yYQYgMRiaNGjQBppoAAAAkREAJoCAEyGhop6NU/QRsiJ6TR6gNAAyNp3IfFE+iB8wWf4sL9xK/9s+3qKxh/7ZVFEGIyI/5JWLFO2ggni0GfE1j/q180JWT7aezyLxgxVPBQ/81h+V+RO+FUUUiqHaG/PSpx6ZY549csBIorBIMWAVifJXMP+vf+z0e1k8/d/d+T5/R91DtgpZIi3R058a/zxZvm1jfVqsN0ZtRDL433LaEfpU+TeeEPqzQb6M/6R93vv+mGNequmf9bMv3tdkqNmKFva+eAhdKRAYgFBWMUZFUFWRYsBVEVFkWCrFGICoiz3fx/qfLPln3Ph9wz0/cPyUftzdGyJroxgFBTFZW1B8d8+Ubm9+S2EfZYPm+b9zPv6iYyrNNmIPw4WwIcmqUzfKiPWG/fTdfAmd6Szg2PCvRlrKbh/xpBK9OWFcnOOduywG22NgNkTQ9+ybtgXLtsBXXF4ijHB1LgG+zwbjLylvTaSwuyPacZyExJIJWsY5ebDngtN4t1E9+GuoLEEn4UETkoHanASaGoPGU4HKYyV0PmmhDVpyQzEPNXGN8w88detyeZyRKPUb7eNnZgZ4fjdjW0rTMOOFlC2zmRVjaaG0DZv0x8vHre+yxL7rRzYliDFTNynrbjodq9POFOWjY2WpNocwV25zHErjEeW6Urzt5rwvQsFxZRVurnFBxmHSHFeSfQ6D04d9dhFkWL2HZwO/K+ADtVVVVXwUFpSOjLjnu9sR3JSRd34zZbJUTYzNajJxQz40/X1wIqdzYyhXHBtMW2u8RWW6nvfd00G/aa8CpG3vt62nuAWHGmM/oHH8TlOCZ3GfrIDpvdi8DmGPT59zm+rTjXTeHHMKqnDQRM5+7K9vMftSPGMFPRo1P6/ssmgE+jA0RBkpKObj/OX47vHy1UlWSdr1q1zxWYK8LIlLIXXpzabobmG7hnl9aSxuxm5+yXrBRtjGmO/mPZE/PmXz10Py53w7Hn1nqfUnHt5PNhhQmN2adu/vQNmWfNpPC+aDyd5SlgYzT77Dt2QPXKIcTM9XjBtpAh0I3yQrYhsSisJujhtKO/LH9/0/dPo//+veBdty2Z6w2Jj2MpMJN/yhkFG6bgJJch1bK0a9d3iBxHq2l2+cY7OUDuw+h7vuz2Jfk3nyfLp9fM/nWJVUI2wrl9fJ46R3Xp7Na3X5FJp4S5fFOZmxQhH0eAnDgCeisTE3lO/nZ/KjPZrkQFSTHOjykkpyRQKXk0zMPVWFYMqJWVQNDLCKTxjlIGQw1j7NsYPJ39pllzLMCybL2C13N6CXET70+qBGQFemJVwFrQJ9YdnfJOhsHFMzUxzMnLVKQvAcWoKypEeTXvpml8DdC/STpCJAjMqqMwziwshWOt7C9x1rQOKVqa7SxyCbm3Ss6RtYcewX35+j0/NTo2qfvXyvXWm1bHyfKhV6W5oVwmP7bSq62MqMUB20vKD6kMkSV/Bdk68n3IVcS0q9S1SyLVQIoJdU5OpFc22rAnRmJWSGiX8TI+5dSsSvSZAdUnw3nkmOxYfhIRH8o7pHJkfCk+921pyxtAc8Rbive92vnfur5J28XfMaBbS62N4NRwzGH7d4F9+hp48Cdue+o83GYwq2zuGtHBn0REcVHqmBA/eIU+G8tk6GvaMN2MZsI57pwZYPwspPn2Upur6CI6L4jM4DY3whRdCxNGNI8SfieY6o3Q6tRhfNJgajl9gckkvYz1MXuT1PVJnMOzg7O9g2FGc7Cvxst/HhtL7u5iWrCGPMh76ypah0sgjq77KGzqizWIZsaRuJR45FBvXewmTM6D5q1LW75Y2GBL1ziiD45tKKNtL2/FEaKeavM8JhDEwoXlEUynpuJhSdCtp5xJ7jM0zPdw06crtMDGbvtwNiZYFhGt46bGHnZS0bacmqaBn5LJRlVPKPSQnm1VdaOp0tp6/D39hYGPs263PdI38u3pnaPJxDeUDOS7LY5mQTJU1d/eDmZcbuDjHGWrC5BkeZ7m3FbDtVf3RNAOCA1+WAEePF25g725szQWAUrlis++tqK0OQEWfQjKLYs19VEeOdZ2EDBYEMrY8dXGPz1WwLIV2IFIbqxbTjVUaRDfGOjydG7DU5Nt7U0BosNNfF8xYxBHKpFctuyZDoMt+GGQuteafLXltJ7uy+GPXL25hvSkZ9fqO0DFImQMd7K9jeABjr2nuA769Xq5oeQbxMlg0xzylG4koDMHlBnGIrpnEOm6aRx2vGK/PKn1L1gYlU+R7Mxtyuv9JFGNtt/AaPAdmu6z7+mn29SCgHH/PHeA8g2Rvq2g+PCm+te09LeFe2c+vvatyt26G9TI9FK559BtYYFBeV0SCCNxTrwVjaocQBFUNeN6XOc6C20tYeFJGPW53xE/bG1uGBFcPWLawOJYC7uKyyWAWG0kCNbXdSKez2v6n8GOIPj74RV8N8z67XGsxRoo5sIiaR8q1KM+D0igfXQQvSxKYGvJXn2ev4f7/864VIOiAhwOVkFrCLbZy3g8vio82Mzbn21xnZKnxYvVZzKriUrzjAN7HuzbkJ8nIIj0jWN7/Jt9zntK4PxVGkkPGRSiZ/kLo4BQJd81U+w0BFFX0KXv0V7yMEfOcm8yjglqcagSLCfLsKk5aVFGOFLySBfgLJM4QPQWU7X7BN3sKPWRQ8vwyVhYGKWSz2ioVypzxvZmi7yndo7d6+Uc1VEMfuanu4fLycn9fofsAkhFu/x6/ZH39PLad11r5gEkIwwqASQjsoPN63/ClyX3ucjBxgJIRxft49vx/X/wCSEbn9Oc8mQG8T40gJIRjYUuASQjf0+8BJCKKzy3AxuHEIg8t0zMRtaVk2acmM7i7XW5tjoLlKN0u1Mms5nhzHLj0P/sdL32Q7GCMMcAaJbRr7GGMdEzihUjGRFw1slliiMTdTgLQQYnISmKBioDSbhtrbQdLbbWNYQKLVlhA2LUEEEHYTRdSpVgVELzAlMNmY1ogsUGKS20obW2yjBRbDGIwRMKIJZbHnkep9X/kBJCOXxAJIRs5gEkInnODd0bP1ke2fnASQjL0nEs+4BJCPG/2d3rASQjHO+nXd9Xqz7GNdIM/VRuZUTVc1tUli8/XJ88SIyAdlLIIwpSkEZ0SToaGBkRkh2BSkkRIFKWERkIF501LOHJIIycKWQRkk2FxpEQDoM8EI2lOHAEQ5KWSIkJLhxpBEgfB73kz9oCSEO7Xv4UCSEbpm0TR1sT/uAkhEaOukPne3+Dr9L9gknv34PhXOwpcNSmmmG6UR2RdRg85RM8xxW6rbxu9iUIyPLG8obJ0CIaRIipqFGCCyzDg4IMQssxRRqBScgaYZjQbl0lOKDhZRttaCLLTOq0EiMgs0KEMWYgyJrEqiMkt4FjXOIs1ludW1qpSLHCTUwJrZWsrQosZbjj6eqHx7h3T4iuswzDLTUZkLlsMI3U0xrFrW1uOvx9dETqltiDL7QhrGw9CMIjJwDQgNhAwgIyHAghRIIIxIE8HXIlQqpUwcBgxCGDCRXGIREHcWzEGML0HB6RqW2ToEssGCDJw4hQbWYNpYDMUeZRrD0NnjzNqcoG5uYec5weDuEzxoixpS2tW04mxUsxQqWyBohDXNRRt4nONW14V5Xct4DkbNTINpYwo4shYYKEQRmQFCyazg0trJQgIRbXnMXWi26yCZHTRDQEFFmoJtwZZYltE5JDYSzSNY2AnK0o610ppjCWR0iJRMJSaFVBYUgnMhdf5R6QEkI8gEkIswdwsCAbkbmIawHaxibFwzPF5dZhuu3w3mXhnFDQwZDpdbaPDSsNi2TTmGOU4ji8q5qipylVMJc5xi5WgmRoKMMqUNCs4rVCirZzhiubQWml0EWwxUaEuSKpQu3LKMKlBrIseSQNoMaTZbAssLY2XlWjaFWtTIygMg0aXUicktyDUN0Y6al/R3o/wbb2NEfpZ9lLoPS91dzIsNHnK0rHie/Ud4tW02kfXef3545xR2P9kR/ZC3DBKl0mZ8sITNOWLu7Q5rtivKJ5B5UNMuroR9tK9Dbj8IDDNNTYR8bezsrKiJtIFHYOUyKchD0UpXTItK4Qppcz3/IZ3GsjejG+AHwwXo06fSAkhHul/Kis1ZkegNC/INjo7+b4mxMdSDVkniyCQzDCcfIq7BXI1A0EVponsdlgtig86O1ppRI2XASrRKMWZfw+YSalOMgE23biCRARBDx2XdzFQ1HWkURdTPIORFSJzjorMU+8PXbV1gOqVrCLtOrEDZ+DMO4Hqw5SAYGVjAZZomN2WyZY/qa5jS72lNRyWGcLjcMhVNQ3N9nynKEA2mlUlsC7Bi6QPEB1UqlXwvjW4lFl/wBJCMKDurOUgXBW+tvRJKoWsk4EUVlMjrY1QFDRAZ2rp9AoLCZUhXEWT7oTBG9AZNDTcaVVSoVzB7QEkI05zH01tflgPSw7Y5SDANwSB9Cr+kM07IwqjFTJ1eE71gg1/MBxvdIMWYN0Ihc8SjC0xnbHbKLH2Pz+lEt5Z7Gz6J0j+cn9XcDPXc0k8+BliRJ0NLi26lrWGCy/z8+6iMjBAVpHAkzRGfaCIjCYMSnQDoN6TsSDw32UphoMNa+fPG1g2HTYhaIrecS0Oipsdy77aAHWz6toZoNmdrt6q7hCSEdmg2NP4d2deUqx+u6NlSnuH3YwF+HK+6oWTQys3N6Kzw91pKj6KSb7eSLyITQGYgNWCPYk8GU3gpJtIbqBRWcUFQ7mBam1/ClgJnMk1QWYUOhg1WrZTB3ZZTJWMrYvtjS7P/sBJCLyrJLSErluBrZQVEpF9OlN9syHACPzJuuR3bqPh6azY7nnKgK17NQMkHUg8PbADQZ5CSOq8N3fzm4EdcwijYrSerrIswOBwW7/Dsw1q5Dh6cywhr3Ge824TIzfXotRiejU3l1dUHL+NbuPNVxjMjlRLUxISUIgjNGuCoFoo5Z4XC4oGqmHB92UC7CsuRDREVJ21GcStpow/b1oLMNt+H3klPARic7fibTZIV1oRbt2uwliq8uLUfIyZULhWoC+26LUmTfQRfIBgWgfCvhaAF1ygw5C05hjK9iaB1I6V0b7pQu0E2DQwYxCYU/XWD9Hd4K73zE/jFjQ/yASQj92fnKp59BaHrFoFAp2+BcZ9a3yvGpCgyLEttFTm+gCaU95VFE77iCfJMG2NCea79g6oMixdmMEqxW7Y7ZJaCGBCPih7/VqAsU/QBJCD8KAIPnQ+MjZOSXSzAVDAfeLjFu4tkCbUqNHEZ7t2Bv9LAZuISIGn5AJIQ5w2VCB+5iICpEEaG/p8igWK1kXpgiCH49GHE3pFbDXgid4CSEOc8A0DJvf3kUQj3lQCOjl55d2KRaIqswCvTaGj4sS84xS303rWCTS/313p2gYpWd6bPQXfJrgwGYQRTIDqZKYEGUAuElqBbDFUVURbKj+EYo8//h6Q4moPuPYuOW/kNoozmnnckS8xxYMaZMw9+HHh6gNmnjesy9F0bE0NptiBjY00PyQVP8YNLUapB1PuObjkw7Xlr6X4K68xFxR9WMkgwHnAbS7f4JdFNMS+lxx1c7srrWpPMZgsgCSEW2tG3ieMXWyMgYu2CgIkHCTtVTpjg9S0uvco6AEkIx8Wto1DUCBqH2wQMgqAkhE0fsRjQIDgWjerQjBpF4fl64xKKzbCiyFTlMkREW10DUWKK2GynZy2TGrVcK9XMBoccXCY5EQt1/Rc0d+OzeQZ6IyrE8TEsNTNmi0lgdOVwMyHRpkG8T1QJXRIXM6gXSQ8I/ABJCM6267ThYzX1OeDhgkaZK7K9sWvCDuVOnZkgZJgMjnY02G7fGPKy7mMTHjkT1L6IsDRHSBkAxX7MEHNcP4XBR5AHxGTbQGzZ8c+D5zEOnf7b56z4wUE9Wj7S3g4rS4wiKMVEVQYiB7V5jlT+iBWJ83x4DM6YfB7yWiEc7P5WQUbTvMGBMiYXmOzruu6s6Do45jhhDoBYecPTOKMD1KQ0ApKRnvcN+0JRpCKoDrtlluaJ9YreYDWlvLk+Iw3DA2rw23YpiNWSNoGwDD/UBJCIFVoYS1GZhKSTJpjjXa0zaGh5H0xxSw733kDqiDrMKQzM0N8vMdJhRgzMK0FQdACSEcoI84KBxUVPXRoZ+dbfdtjmXKFO57WkdFaxq0DYkTMNiKBz2/Qa1+E86nRBi4Kn8O3RjrlV4ZMgTlJToipgoQ1Ey8HIKYLmSKARJFeFgcFs1CRrYj1LOZspRH+w0YgYLn5uuiyfIGAu6kZdXGdG6lwMG10Gt/BKXIkgEvQ0AkpWvl3r9r15JWXcBof39x07tB+7NHf38SrGztIMweKnnVy3UkEDJi0dAwswywE2/H4s2gaBAfa99M7D9YCSESTURiSy615QKWPROIirJPeSj6NDzb2cMFkSHzPHnu0OkrZ5gfAAyZjkgMOwApfesXmMlMuS452TWCsXxBgjLvFoKvNke8uRQ6w8sFoMdc4SS7Hhy0s1BrHz1mlCqZYoPYbbEVEPFMAYAbbkXCL/P7FXEe1Y8rssQ/eoDUJAgLtEENLRpp4+XzuKFW3YIsRHmASQjvy8KSV+4tUXBkw+y+uNbJg4QoLWQehm+63AZ42WTOcWuiqyI9EBJyeWw1uAvgrBOCjC2+mcYDLmXwoAYr0EPwk0IXrHV2Dt+T3/7fpldc/rVGzHA+qhukKMWU583HNYKjVRSpWoZHWIgpwu5eAv0muepci4nSdvQlS9PHnXA0ejOQgqSFCSpMnBCYgn9JR8PVaqe7VnD2VVdbPHd8dp2p3fxPk5/dDq4TxWJ0nB0xXSmMYoxi+XLMk5sgkyhzWbijc1GvlTGRVVfN7Z7GC1Dp8Xt2njXcNnaebxnK0OIjiyeHnC1aFiDBEJ55y+dR7eng0FL6nNziluvgSEdZxqKD5h0eNwV6fRpLfN5VVVHjaUqqtotVALs4crYLPp83Gq3utvRHxsFp1OdNcr1FUfDbn17mpa7wgLeSnq5muaOwOId6BxhhwdTLmY8Fl+4d+FmFqKBlhTggJA2LuCwpU+V+HGmWCRdjCY0A2se3Zw2LLeb4/QOnAC83C2XA2JgiVCAab0IjSI4gQBuMMPCbMDdGGN5hvst5JlK35ZKBPiKrVqp7Cp8GEtS2C4HSQO223ZY70KjzUkImLtbM3DDi77CMEQouawZEXzJYxY4upY0zo44DpvCaC0B3ffOToQ6YznRoEkEVSZiz6SBdM3ZAXWh83XXPOmFt44kJrNM6NwyrszgXovNt2KP1MIenUwfZlmFb8QRckwMWNSgOdrsLbMUaKKbanR7uN/WcOIcWmNBag3UNvDec9KOprJMaRKmIpE5JVBO9lESmi9IgsjoOznRRLEdyHwzNBrNiIQPfEj6UjW1OQApkZYriXq7nQYVDpoAUz1XP7AEkI1KSqn4CI1g/EftB2TouvLrQxe94u06rENf6XHR3qKTMouuW6vbbIAdoWgV6x180cB6+jhd0DMLyzmRj0mcfgxtKFcYLZyu17e3b9gCSEOlNmXmeUdib43ed1yKudKY+kpOTCi9mVanIsF0ULblCX9oCSEVFlcFNTqP2bs7RfFo7QEkIZAdnmO+OPVKlOYV1a1okrWIkIVZoiUDRDXP1wpgzASQj12XXCsNUd3Mihj9dkM2/MzGoRwMGA+vUBJCLS4yTkutg1BQ35M/2NxN/cdl7fJtoq7ZuI+C+Hzb4t/vT9c86lPEgQJyGUD+BdyRThQkMngzx4=')))
homework_2/testClasses.py
# testClasses.py
# --------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
# import modules from python standard library
import inspect
import re
import sys
# Class which models a question in a project. Note that questions have a
# maximum number of points they are worth, and are composed of a series of
# test cases
class Question(object):
def raiseNotDefined(self):
print('Method not implemented: %s' % inspect.stack()[1][3])
sys.exit(1)
def __init__(self, questionDict, display):
self.maxPoints = int(questionDict['max_points'])
self.testCases = []
self.display = display
def getDisplay(self):
return self.display
def getMaxPoints(self):
return self.maxPoints
# Note that 'thunk' must be a function which accepts a single argument,
# namely a 'grading' object
def addTestCase(self, testCase, thunk):
self.testCases.append((testCase, thunk))
def execute(self, grades):
self.raiseNotDefined()
# Question in which all test cases must be passed in order to receive credit
class PassAllTestsQuestion(Question):
def execute(self, grades):
# TODO: is this the right way to use grades? The autograder doesn't seem to use it.
testsFailed = False
grades.assignZeroCredit()
for _, f in self.testCases:
if not f(grades):
testsFailed = True
if testsFailed:
grades.fail("Tests failed.")
else:
grades.assignFullCredit()
class ExtraCreditPassAllTestsQuestion(Question):
def __init__(self, questionDict, display):
Question.__init__(self, questionDict, display)
self.extraPoints = int(questionDict['extra_points'])
def execute(self, grades):
# TODO: is this the right way to use grades? The autograder doesn't seem to use it.
testsFailed = False
grades.assignZeroCredit()
for _, f in self.testCases:
if not f(grades):
testsFailed = True
if testsFailed:
grades.fail("Tests failed.")
else:
grades.assignFullCredit()
grades.addPoints(self.extraPoints)
# Question in which predict credit is given for test cases with a ``points'' property.
# All other tests are mandatory and must be passed.
class HackedPartialCreditQuestion(Question):
def execute(self, grades):
# TODO: is this the right way to use grades? The autograder doesn't seem to use it.
grades.assignZeroCredit()
points = 0
passed = True
for testCase, f in self.testCases:
testResult = f(grades)
if "points" in testCase.testDict:
if testResult:
points += float(testCase.testDict["points"])
else:
passed = passed and testResult
# FIXME: Below terrible hack to match q3's logic
if int(points) == self.maxPoints and not passed:
grades.assignZeroCredit()
else:
grades.addPoints(int(points))
class Q6PartialCreditQuestion(Question):
"""Fails any test which returns False, otherwise doesn't effect the grades object.
Partial credit tests will add the required points."""
def execute(self, grades):
grades.assignZeroCredit()
results = []
for _, f in self.testCases:
results.append(f(grades))
if False in results:
grades.assignZeroCredit()
class PartialCreditQuestion(Question):
"""Fails any test which returns False, otherwise doesn't effect the grades object.
Partial credit tests will add the required points."""
def execute(self, grades):
grades.assignZeroCredit()
for _, f in self.testCases:
if not f(grades):
grades.assignZeroCredit()
grades.fail("Tests failed.")
return False
class NumberPassedQuestion(Question):
"""Grade is the number of test cases passed."""
def execute(self, grades):
grades.addPoints([f(grades) for _, f in self.testCases].count(True))
# Template modeling a generic test case
class TestCase(object):
def raiseNotDefined(self):
print('Method not implemented: %s' % inspect.stack()[1][3])
sys.exit(1)
def getPath(self):
return self.path
def __init__(self, question, testDict):
self.question = question
self.testDict = testDict
self.path = testDict['path']
self.messages = []
def __str__(self):
self.raiseNotDefined()
def execute(self, grades, moduleDict, solutionDict):
self.raiseNotDefined()
def writeSolution(self, moduleDict, filePath):
self.raiseNotDefined()
return True
# Tests should call the following messages for grading
# to ensure a uniform format for test output.
#
# TODO: this is hairy, but we need to fix grading.py's interface
# to get a nice hierarchical project - question - test structure,
# then these should be moved into Question proper.
def testPass(self, grades):
grades.addMessage('PASS: %s' % (self.path,))
for line in self.messages:
grades.addMessage(' %s' % (line,))
return True
def testFail(self, grades):
grades.addMessage('FAIL: %s' % (self.path,))
for line in self.messages:
grades.addMessage(' %s' % (line,))
return False
# This should really be question level?
def testPartial(self, grades, points, maxPoints):
grades.addPoints(points)
extraCredit = max(0, points - maxPoints)
regularCredit = points - extraCredit
grades.addMessage('%s: %s (%s of %s points)' % (
"PASS" if points >= maxPoints else "FAIL", self.path, regularCredit, maxPoints))
if extraCredit > 0:
grades.addMessage('EXTRA CREDIT: %s points' % (extraCredit,))
for line in self.messages:
grades.addMessage(' %s' % (line,))
return True
def addMessage(self, message):
self.messages.extend(message.split('\n'))
homework_2/testParser.py
# testParser.py
# -------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
import re
import sys
class TestParser(object):
def __init__(self, path):
# save the path to the test file
self.path = path
def removeComments(self, rawlines):
# remove any portion of a line following a '#' symbol
fixed_lines = []
for l in rawlines:
idx = l.find('#')
if idx == -1:
fixed_lines.append(l)
else:
fixed_lines.append(l[0:idx])
return '\n'.join(fixed_lines)
def parse(self):
# read in the test case and remove comments
test = {}
with open(self.path) as handle:
raw_lines = handle.read().split('\n')
test_text = self.removeComments(raw_lines)
test['__raw_lines__'] = raw_lines
test['path'] = self.path
test['__emit__'] = []
lines = test_text.split('\n')
i = 0
# read a property in each loop cycle
while(i < len(lines)):
# skip blank lines
if re.match('\A\s*\Z', lines[i]):
test['__emit__'].append(("raw", raw_lines[i]))
i += 1
continue
m = re.match('\A([^"]*?):\s*"([^"]*)"\s*\Z', lines[i])
if m:
test[m.group(1)] = m.group(2)
test['__emit__'].append(("oneline", m.group(1)))
i += 1
continue
m = re.match('\A([^"]*?):\s*"""\s*\Z', lines[i])
if m:
msg = []
i += 1
while(not re.match('\A\s*"""\s*\Z', lines[i])):
msg.append(raw_lines[i])
i += 1
test[m.group(1)] = '\n'.join(msg)
test['__emit__'].append(("multiline", m.group(1)))
i += 1
continue
print('error parsing test file: %s' % self.path)
sys.exit(1)
return test
def emitTestDict(testDict, handle):
for kind, data in testDict['__emit__']:
if kind == "raw":
handle.write(data + "\n")
elif kind == "oneline":
handle.write('%s: "%s"\n' % (data, testDict[data]))
elif kind == "multiline":
handle.write('%s: """\n%s\n"""\n' % (data, testDict[data]))
else:
raise Exception("Bad __emit__")
homework_2/test_cases/CONFIG
order: "q1 q2 q3 q4 q5"
homework_2/test_cases/extra/CONFIG
max_points: "0"
class: "PartialCreditQuestion"
homework_2/test_cases/extra/grade-agent.test
class: "EvalAgentTest"
agentName: "ContestAgent"
layoutName: "contestClassic"
maxTime: "180"
numGames: "5"
scoreThresholds: "2500 2900"
randomSeed: "0"
ghosts: "[DirectionalGhost(1), DirectionalGhost(2), DirectionalGhost(3)]"
homework_2/test_cases/q1/CONFIG
max_points: "4"
class: "PartialCreditQuestion"
homework_2/test_cases/q1/grade-agent.solution
# This is the solution file for test_cases/q1/grade-agent.test.
# File intentionally blank.
homework_2/test_cases/q1/grade-agent.test
class: "EvalAgentTest"
agentName: "ReflexAgent"
layoutName: "openClassic"
maxTime: "120"
numGames: "10"
nonTimeoutMinimum: "10"
scoreThresholds: "500 1000"
winsMinimum: "1"
winsThresholds: "5 10"
randomSeed: "0"
ghosts: "[RandomGhost(1)]"
homework_2/test_cases/q2/0-eval-function-lose-states-1.solution
# This is the solution file for test_cases/q2/0-eval-function-lose-states-1.test.
action: "Left"
generated: "lose1 lose2 root"
homework_2/test_cases/q2/0-eval-function-lose-states-1.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
root
/ \
lose1 lose2
1 0
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on losing states.
"""
num_agents: "2"
start_state: "root"
win_states: ""
lose_states: "lose1 lose2"
successors: """
root Left lose1
root Right lose2
"""
evaluation: """
lose1 1.0
lose2 0.0
"""
homework_2/test_cases/q2/0-eval-function-lose-states-2.solution
# This is the solution file for test_cases/q2/0-eval-function-lose-states-2.test.
action: "Right"
generated: "lose1 lose2 root"
homework_2/test_cases/q2/0-eval-function-lose-states-2.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
root
/ \
lose1 lose2
0 1
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on losing states.
"""
num_agents: "2"
start_state: "root"
win_states: ""
lose_states: "lose1 lose2"
successors: """
root Left lose1
root Right lose2
"""
evaluation: """
lose1 0.0
lose2 1.0
"""
homework_2/test_cases/q2/0-eval-function-win-states-1.solution
# This is the solution file for test_cases/q2/0-eval-function-win-states-1.test.
action: "Left"
generated: "root win1 win2"
homework_2/test_cases/q2/0-eval-function-win-states-1.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
root
/ \
win1 win2
1 0
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on winning states.
"""
num_agents: "2"
start_state: "root"
win_states: "win1 win2"
lose_states: ""
successors: """
root Left win1
root Right win2
"""
evaluation: """
win1 1.0
win2 0.0
"""
homework_2/test_cases/q2/0-eval-function-win-states-2.solution
# This is the solution file for test_cases/q2/0-eval-function-win-states-2.test.
action: "Right"
generated: "root win1 win2"
homework_2/test_cases/q2/0-eval-function-win-states-2.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
root
/ \
win1 win2
0 1
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on winning states.
"""
num_agents: "2"
start_state: "root"
win_states: "win1 win2"
lose_states: ""
successors: """
root Left win1
root Right win2
"""
evaluation: """
win1 0.0
win2 1.0
"""
homework_2/test_cases/q2/0-lecture-6-tree.solution
# This is the solution file for test_cases/q2/0-lecture-6-tree.test.
action: "Center"
generated: "A B C D E F G H I max min1 min2 min3"
homework_2/test_cases/q2/0-lecture-6-tree.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
# Tree from lecture 6 slides
diagram: """
max
/-/ | \--\
/ | \
/ | \
min1 min2 min3
/|\ /|\ /|\
/ | \ / | \ / | \
A B C D E F G H I
3 12 8 5 4 6 14 1 11
"""
num_agents: "2"
start_state: "max"
win_states: "A B C D E F G H I"
lose_states: ""
successors: """
max Left min1
max Center min2
max Right min3
min1 Left A
min1 Center B
min1 Right C
min2 Left D
min2 Center E
min2 Right F
min3 Left G
min3 Center H
min3 Right I
"""
evaluation: """
A 3.0
B 12.0
C 8.0
D 5.0
E 4.0
F 6.0
G 14.0
H 1.0
I 11.0
"""
homework_2/test_cases/q2/0-small-tree.solution
# This is the solution file for test_cases/q2/0-small-tree.test.
action: "pacLeft"
generated: "A B C D deeper minLeft minRight root"
homework_2/test_cases/q2/0-small-tree.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
root
/ \
minLeft minRight
/ \ / \
A B C deeper
4 3 2 |
D
1000
"""
num_agents: "2"
start_state: "root"
win_states: "A C"
lose_states: "B D"
successors: """
root pacLeft minLeft
root pacRight minRight
minLeft gLeft A
minLeft gRight B
minRight gLeft C
minRight gRight deeper
deeper pacLeft D
"""
evaluation: """
A 4.0
B 3.0
C 2.0
D 1000.0
"""
homework_2/test_cases/q2/1-1-minmax.solution
# This is the solution file for test_cases/q2/1-1-minmax.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q2/1-1-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -3.01
a - max
b - min
c - max
Note that the minimax value of b1 is -3.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -3.01
"""
homework_2/test_cases/q2/1-2-minmax.solution
# This is the solution file for test_cases/q2/1-2-minmax.test.
action: "Right"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q2/1-2-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -2.99
a - max
b - min
c - max
Note that the minimax value of b1 is -3.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -2.99
"""
homework_2/test_cases/q2/1-3-minmax.solution
# This is the solution file for test_cases/q2/1-3-minmax.test.
action: "Left"
generated: "a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q2/1-3-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
4.01 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b2 is 4.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 4.01
"""
homework_2/test_cases/q2/1-4-minmax.solution
# This is the solution file for test_cases/q2/1-4-minmax.test.
action: "Right"
generated: "a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q2/1-4-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
3.99 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b2 is 4.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 3.99
"""
homework_2/test_cases/q2/1-5-minmax.solution
# This is the solution file for test_cases/q2/1-5-minmax.test.
action: "Right"
generated: "A B C D E F G H Z a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q2/1-5-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
/ \ / \ / \ / \ |
A B C D E F G H Z
-3 13 5 9 10 3 -6 8 3.01
a - max
b - min
c - max
d - min
Note the minimax value of b1 is 3.
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
A -3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 3.0
G -6.0
H 8.0
Z 3.01
"""
homework_2/test_cases/q2/1-6-minmax.solution
# This is the solution file for test_cases/q2/1-6-minmax.test.
action: "Left"
generated: "A B C D E F G H Z a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q2/1-6-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
/ \ / \ / \ / \ |
A B C D E F G H Z
-3 13 5 9 10 3 -6 8 2.99
a - max
b - min
c - max
d - min
Note the minimax value of b1 is 3.
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
A -3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 3.0
G -6.0
H 8.0
Z 2.99
"""
homework_2/test_cases/q2/1-7-minmax.solution
# This is the solution file for test_cases/q2/1-7-minmax.test.
action: "Left"
generated: "I J K L M N O P Z a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q2/1-7-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
| / \ / \ / \ / \
Z I J K L M N O P
-1.99 -1 -9 4 7 2 5 -3 -2
a - max
b - min
c - min
d - max
Note that the minimax value of b2 is -2
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
I -1.0
J -9.0
K 4.0
L 7.0
M 2.0
N 5.0
O -3.0
P -2.0
Z -1.99
"""
homework_2/test_cases/q2/1-8-minmax.solution
# This is the solution file for test_cases/q2/1-8-minmax.test.
action: "Right"
generated: "I J K L M N O P Z a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q2/1-8-minmax.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
| / \ / \ / \ / \
Z I J K L M N O P
-2.01 -1 -9 4 7 2 5 -3 -2
a - max
b - min
c - min
d - max
Note that the minimax value of b2 is -2.01
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
I -1.0
J -9.0
K 4.0
L 7.0
M 2.0
N 5.0
O -3.0
P -2.0
Z -2.01
"""
homework_2/test_cases/q2/2-1a-vary-depth.solution
# This is the solution file for test_cases/q2/2-1a-vary-depth.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx"
homework_2/test_cases/q2/2-1a-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -4.01
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -4.01
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -4.01
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -4.01
"""
homework_2/test_cases/q2/2-1b-vary-depth.solution
# This is the solution file for test_cases/q2/2-1b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q2/2-1b-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -4.01
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -4.01
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -4.01
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -4.01
"""
homework_2/test_cases/q2/2-2a-vary-depth.solution
# This is the solution file for test_cases/q2/2-2a-vary-depth.test.
action: "Right"
generated: "a b1 b2 c1 c2 cx"
homework_2/test_cases/q2/2-2a-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -3.99
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -3.99
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -3.99
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -3.99
"""
homework_2/test_cases/q2/2-2b-vary-depth.solution
# This is the solution file for test_cases/q2/2-2b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q2/2-2b-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -3.99
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -3.99
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -3.99
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -3.99
"""
homework_2/test_cases/q2/2-3a-vary-depth.solution
# This is the solution file for test_cases/q2/2-3a-vary-depth.test.
action: "Left"
generated: "a b1 b2 c3 c4 cx"
homework_2/test_cases/q2/2-3a-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
5.01 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
5.01 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 5.01
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 5.01
"""
homework_2/test_cases/q2/2-3b-vary-depth.solution
# This is the solution file for test_cases/q2/2-3b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q2/2-3b-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
5.01 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
5.01 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 5.01
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 5.01
"""
homework_2/test_cases/q2/2-4a-vary-depth.solution
# This is the solution file for test_cases/q2/2-4a-vary-depth.test.
action: "Right"
generated: "a b1 b2 c3 c4 cx"
homework_2/test_cases/q2/2-4a-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
4.99 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
4.99 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 4.99
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 4.99
"""
homework_2/test_cases/q2/2-4b-vary-depth.solution
# This is the solution file for test_cases/q2/2-4b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q2/2-4b-vary-depth.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
4.99 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
4.99 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 4.99
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 4.99
"""
homework_2/test_cases/q2/2-one-ghost-3level.solution
# This is the solution file for test_cases/q2/2-one-ghost-3level.test.
action: "Left"
generated: "a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q2/2-one-ghost-3level.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
3 9 10 6 4 7 0 5
a - max
b - min
c - max
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
"""
evaluation: """
d1 3.0
d2 9.0
d3 10.0
d4 6.0
d5 4.0
d6 7.0
d7 0.0
d8 5.0
"""
homework_2/test_cases/q2/3-one-ghost-4level.solution
# This is the solution file for test_cases/q2/3-one-ghost-4level.test.
action: "Left"
generated: "A B C D E F G H I J K L M N O P a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q2/3-one-ghost-4level.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
/ \ / \ / \ / \ / \ / \ / \ / \
A B C D E F G H I J K L M N O P
3 13 5 9 10 11 6 8 1 0 4 7 12 15 2 14
a - max
b - min
c - max
d - min
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
"""
evaluation: """
A 3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 11.0
G 6.0
H 8.0
I 1.0
J 0.0
K 4.0
L 7.0
M 12.0
N 15.0
O 2.0
P 14.0
"""
homework_2/test_cases/q2/4-two-ghosts-3level.solution
# This is the solution file for test_cases/q2/4-two-ghosts-3level.test.
action: "Left"
generated: "a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q2/4-two-ghosts-3level.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
3 9 10 6 4 7 0 5
a - max
b - min
c - min
"""
num_agents: "3"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
"""
evaluation: """
d1 3.0
d2 9.0
d3 10.0
d4 6.0
d5 4.0
d6 7.0
d7 0.0
d8 5.0
"""
homework_2/test_cases/q2/5-two-ghosts-4level.solution
# This is the solution file for test_cases/q2/5-two-ghosts-4level.test.
action: "Left"
generated: "A B C D E F G H I J K L M N O P a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q2/5-two-ghosts-4level.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
/ \ / \ / \ / \ / \ / \ / \ / \
A B C D E F G H I J K L M N O P
3 13 5 9 10 11 6 8 1 0 4 7 12 15 2 14
a - max
b - min
c - min
d - max
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
"""
evaluation: """
A 3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 11.0
G 6.0
H 8.0
I 1.0
J 0.0
K 4.0
L 7.0
M 12.0
N 15.0
O 2.0
P 14.0
"""
homework_2/test_cases/q2/6-tied-root.solution
# This is the solution file for test_cases/q2/6-tied-root.test.
action: "Left"
generated: "A B C max min1 min2"
homework_2/test_cases/q2/6-tied-root.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
max
/ \
min1 min2
| / \
A B C
10 10 0
"""
num_agents: "2"
start_state: "max"
win_states: "A B"
lose_states: "C"
successors: """
max Left min1
max Right min2
min1 Down A
min2 Left B
min2 Right C
"""
evaluation: """
A 10.0
B 10.0
C 0.0
"""
homework_2/test_cases/q2/7-1a-check-depth-one-ghost.solution
# This is the solution file for test_cases/q2/7-1a-check-depth-one-ghost.test.
action: "Left"
generated: "a b1 b2 b3 c1 c2 c3"
homework_2/test_cases/q2/7-1a-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "1"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 1, the evaluation function is called at level c,
so Left should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q2/7-1b-check-depth-one-ghost.solution
# This is the solution file for test_cases/q2/7-1b-check-depth-one-ghost.test.
action: "Center"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3"
homework_2/test_cases/q2/7-1b-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 2, the evaluation function is called at level e,
so Center should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q2/7-1c-check-depth-one-ghost.solution
# This is the solution file for test_cases/q2/7-1c-check-depth-one-ghost.test.
action: "Right"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3"
homework_2/test_cases/q2/7-1c-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 3, the evaluation function is called at level g,
so Right should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q2/7-2a-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q2/7-2a-check-depth-two-ghosts.test.
action: "Left"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3"
homework_2/test_cases/q2/7-2a-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "1"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 1, the evaluation function is called at level d,
so Left should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q2/7-2b-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q2/7-2b-check-depth-two-ghosts.test.
action: "Center"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3"
homework_2/test_cases/q2/7-2b-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "2"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 2, the evaluation function is called at level g,
so Center should be returned. If your algorithm is returning
a different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q2/7-2c-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q2/7-2c-check-depth-two-ghosts.test.
action: "Right"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3 h1 h2 h3 i1 i2 i3 j1 j2 j3"
homework_2/test_cases/q2/7-2c-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "MinimaxAgent"
depth: "3"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 3, the evaluation function is called at level j,
so Right should be returned. If your algorithm is returning
a different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q2/8-pacman-game.solution
optimalActions: """
[[["West", "East"], 59], [["West", "East"], 35]]
[[["West"], 190], [["West"], 127]]
[[["West"], 190], [["West"], 135]]
[[["West", "North"], 120], [["West", "North"], 82]]
[[["West"], 77], [["West"], 57]]
[[["West", "North"], 143], [["West", "North"], 97]]
[[["West"], 155], [["West"], 110]]
[[["West"], 40], [["West"], 27]]
[[["North"], 64], [["North"], 43]]
[[["North"], 85], [["North"], 57]]
[[["North"], 106], [["North"], 71]]
[[["North"], 97], [["North"], 65]]
[[["Stop", "East"], 154], [["East"], 103]]
[[["East"], 156], [["East"], 101]]
[[["West"], 30], [["West"], 17]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["North"], 29], [["North"], 18]]
[[["North"], 50], [["North"], 31]]
[[["West"], 55], [["West"], 36]]
[[["East"], 29], [["East"], 16]]
[[["North"], 89], [["North"], 61]]
[[["East", "North"], 161], [["East", "North"], 121]]
[[["East", "North"], 221], [["East", "North"], 166]]
[[["North", "South"], 105], [["North", "South"], 77]]
[[["West"], 69], [["West"], 51]]
[[["West"], 94], [["West"], 69]]
[[["West", "Stop"], 57], [["West"], 42]]
[[["West", "Stop", "East"], 69], [["West", "East"], 49]]
[[["West", "Stop", "East"], 61], [["West", "East"], 41]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 28], [["East", "South"], 19]]
[[["Stop", "East", "South"], 34], [["East", "South"], 23]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 64], [["East", "South"], 43]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 102], [["East", "South"], 67]]
[[["Stop", "South"], 23], [["South"], 13]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East", "North"], 29], [["East", "North"], 18]]
[[["East"], 38], [["East"], 22]]
[[["North"], 29], [["North"], 18]]
[[["North"], 38], [["North"], 22]]
[[["East"], 33], [["East"], 22]]
[[["East"], 37], [["East"], 18]]
[[["East"], 18], [["East"], 12]]
[[["East"], 37], [["East"], 26]]
[[["East"], 69], [["East"], 41]]
[[["East"], 56], [["East"], 26]]
[[["East"], 44], [["East"], 29]]
[[["North", "South"], 83], [["North", "South"], 52]]
[[["East", "North"], 121], [["East", "North"], 74]]
[[["East", "North"], 97], [["East", "North"], 73]]
[[["North", "South"], 173], [["North", "South"], 130]]
[[["West", "East"], 90], [["West", "East"], 66]]
[[["West", "Stop", "East"], 161], [["West", "East"], 118]]
[[["Stop", "East", "South"], 58], [["East", "South"], 43]]
[[["Stop", "East"], 120], [["South"], 85]]
[[["East"], 78], [["East"], 45]]
[[["West"], 77], [["West"], 42]]
[[["South"], 83], [["South"], 48]]
[[["South"], 49], [["South"], 37]]
[[["South"], 185], [["South"], 104]]
[[["South"], 68], [["South"], 41]]
[[["West"], 30], [["West"], 18]]
[[["West"], 56], [["West"], 29]]
[[["West"], 14], [["West"], 10]]
[[["West"], 20], [["West"], 14]]
[[["West"], 13], [["West"], 9]]
[[["West"], 13], [["West"], 9]]
[[["West"], 16], [["West"], 12]]
[[["West", "North"], 30], [["West", "North"], 20]]
[[["West"], 38], [["West"], 23]]
[[["West", "Stop", "East", "North"], 70], [["West", "East", "North"], 46]]
[[["West", "Stop", "East"], 128], [["West", "East"], 89]]
[[["West", "Stop", "East"], 31], [["West", "East"], 20]]
[[["Stop", "East", "North"], 69], [["East", "North"], 45]]
[[["Stop", "North"], 58], [["North"], 31]]
[[["North"], 34], [["North"], 19]]
[[["North"], 30], [["North"], 17]]
[[["North"], 19], [["North"], 11]]
[[["North"], 34], [["North"], 19]]
[[["East"], 30], [["East"], 17]]
[[["East"], 19], [["East"], 11]]
[[["East"], 44], [["East"], 29]]
[[["East", "South"], 87], [["East", "South"], 60]]
[[["East", "South"], 108], [["East", "South"], 62]]
[[["South"], 120], [["South"], 61]]
[[["North", "South"], 209], [["North", "South"], 132]]
[[["West"], 108], [["West"], 60]]
[[["West", "Stop", "East", "South"], 83], [["West", "East", "South"], 61]]
[[["West", "Stop", "East", "South"], 90], [["West", "East", "South"], 66]]
[[["West", "Stop", "East"], 134], [["West", "East"], 95]]
[[["West", "Stop", "East"], 82], [["West", "East"], 55]]
[[["Stop", "East", "South"], 142], [["East", "South"], 95]]
[[["Stop", "East", "South"], 98], [["East", "South"], 65]]
[[["Stop", "East", "South"], 128], [["East", "South"], 86]]
[[["Stop", "East", "South"], 82], [["East", "South"], 55]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 190], [["East", "South"], 127]]
[[["Stop", "East", "South"], 158], [["East", "South"], 103]]
[[["Stop", "South"], 50], [["South"], 27]]
[[["South"], 30], [["South"], 17]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East", "North"], 29], [["East", "North"], 18]]
[[["East"], 37], [["East"], 22]]
[[["East", "North"], 41], [["East", "North"], 24]]
[[["East"], 59], [["East"], 29]]
[[["East"], 19], [["East"], 11]]
[[["East"], 26], [["East"], 15]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East"], 29], [["East"], 18]]
[[["East"], 37], [["East"], 22]]
[[["East", "North"], 41], [["East", "North"], 24]]
[[["East"], 59], [["East"], 29]]
[[["East"], 19], [["East"], 11]]
[[["North"], 26], [["North"], 15]]
[[["North"], 19], [["North"], 11]]
[[["North"], 30], [["North"], 17]]
[[["North"], 34], [["North"], 19]]
[[["West"], 34], [["West"], 19]]
[[["West"], 25], [["West"], 13]]
[[["West", "Stop", "East"], 7], [["West", "East"], 3]]
"""
altDepthActions: """
[["West", "East"], ["West", "East"], ["West", "East"], ["West", "East"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["Stop", "North"], ["North"]]
[["East"], ["East"], ["Stop", "East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["West"], ["West"], ["West"], ["West"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["West"], ["West"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"], ["North"], ["North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West", "Stop"], ["West"]]
[["West"], ["West"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"], ["Stop", "South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North", "South"], ["North", "South"], ["South"], ["South"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"], ["North"], ["North"]]
[["West", "East"], ["West", "East"], ["East"], ["East"]]
[["West"], ["West"], ["East"], ["East"]]
[["Stop", "East", "South"], ["East", "South"], ["East"], ["East"]]
[["Stop", "East"], ["East"], ["Stop", "East"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["West"], ["West"], ["West"], ["West"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["West", "East"], ["West", "East"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East", "North"], ["West", "East", "North"], ["West", "Stop", "East", "North"], ["West", "East", "North"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "North"], ["East", "North"], ["Stop", "East", "North"], ["East", "North"]]
[["Stop", "North"], ["North"], ["Stop", "North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "South"], ["East", "South"], ["East", "South"], ["East", "South"]]
[["East", "South"], ["East", "South"], ["East", "South"], ["East", "South"]]
[["South"], ["South"], ["South"], ["South"]]
[["North", "South"], ["North", "South"], ["North", "South"], ["North", "South"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"], ["Stop", "South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
"""
partialPlyBugActions: """
[["West", "East"], ["West", "East"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["Stop", "East"], ["East"]]
[["West"], ["West"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["West"], ["West"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "Stop"], ["West"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North", "South"], ["North", "South"]]
[["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"]]
[["West", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East"], ["East"]]
[["East"], ["East"]]
[["West"], ["West"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West", "Stop", "East", "North"], ["West", "East", "North"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "North"], ["East", "North"]]
[["Stop", "North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "South"], ["East", "South"]]
[["East", "South"], ["East", "South"]]
[["South"], ["South"]]
[["North", "South"], ["North", "South"]]
[["West"], ["West"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "Stop", "East"], ["West", "East"]]
"""
homework_2/test_cases/q2/8-pacman-game.test
class: "PacmanGameTreeTest"
alg: "MinimaxAgent"
seed: "0"
depth: "2"
max_points: "4"
# The following specifies the layout to be used
layoutName: "smallClassic"
layout: """
%%%%%%%%%%%%%%%%%%%%
%......%G G%......%
%.%%...%% %%...%%.%
%.%o.%........%.o%.%
%.%%.%.%%%%%%.%.%%.%
%........P.........%
%%%%%%%%%%%%%%%%%%%%
"""
homework_2/test_cases/q2/CONFIG
max_points: "5"
class: "PassAllTestsQuestion"
homework_2/test_cases/q3/0-eval-function-lose-states-1.solution
# This is the solution file for test_cases/q3/0-eval-function-lose-states-1.test.
action: "Left"
generated: "lose1 lose2 root"
homework_2/test_cases/q3/0-eval-function-lose-states-1.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
root
/ \
lose1 lose2
1 0
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on losing states.
"""
num_agents: "2"
start_state: "root"
win_states: ""
lose_states: "lose1 lose2"
successors: """
root Left lose1
root Right lose2
"""
evaluation: """
lose1 1.0
lose2 0.0
"""
homework_2/test_cases/q3/0-eval-function-lose-states-2.solution
# This is the solution file for test_cases/q3/0-eval-function-lose-states-2.test.
action: "Right"
generated: "lose1 lose2 root"
homework_2/test_cases/q3/0-eval-function-lose-states-2.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
root
/ \
lose1 lose2
0 1
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on losing states.
"""
num_agents: "2"
start_state: "root"
win_states: ""
lose_states: "lose1 lose2"
successors: """
root Left lose1
root Right lose2
"""
evaluation: """
lose1 0.0
lose2 1.0
"""
homework_2/test_cases/q3/0-eval-function-win-states-1.solution
# This is the solution file for test_cases/q3/0-eval-function-win-states-1.test.
action: "Left"
generated: "root win1 win2"
homework_2/test_cases/q3/0-eval-function-win-states-1.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
root
/ \
win1 win2
1 0
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on winning states.
"""
num_agents: "2"
start_state: "root"
win_states: "win1 win2"
lose_states: ""
successors: """
root Left win1
root Right win2
"""
evaluation: """
win1 1.0
win2 0.0
"""
homework_2/test_cases/q3/0-eval-function-win-states-2.solution
# This is the solution file for test_cases/q3/0-eval-function-win-states-2.test.
action: "Right"
generated: "root win1 win2"
homework_2/test_cases/q3/0-eval-function-win-states-2.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
root
/ \
win1 win2
0 1
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on winning states.
"""
num_agents: "2"
start_state: "root"
win_states: "win1 win2"
lose_states: ""
successors: """
root Left win1
root Right win2
"""
evaluation: """
win1 0.0
win2 1.0
"""
homework_2/test_cases/q3/0-lecture-6-tree.solution
# This is the solution file for test_cases/q3/0-lecture-6-tree.test.
action: "Center"
generated: "A B C D E F G H max min1 min2 min3"
homework_2/test_cases/q3/0-lecture-6-tree.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
# Tree from lecture 6 slides
diagram: """
max
/-/ | \--\
/ | \
/ | \
min1 min2 min3
/|\ /|\ /|\
/ | \ / | \ / | \
A B C D E F G H I
3 12 8 5 4 6 14 1 11
"""
num_agents: "2"
start_state: "max"
win_states: "A B C D E F G H I"
lose_states: ""
successors: """
max Left min1
max Center min2
max Right min3
min1 Left A
min1 Center B
min1 Right C
min2 Left D
min2 Center E
min2 Right F
min3 Left G
min3 Center H
min3 Right I
"""
evaluation: """
A 3.0
B 12.0
C 8.0
D 5.0
E 4.0
F 6.0
G 14.0
H 1.0
I 11.0
"""
homework_2/test_cases/q3/0-small-tree.solution
# This is the solution file for test_cases/q3/0-small-tree.test.
action: "pacLeft"
generated: "A B C minLeft minRight root"
homework_2/test_cases/q3/0-small-tree.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
root
/ \
minLeft minRight
/ \ / \
A B C deeper
4 3 2 |
D
1000
"""
num_agents: "2"
start_state: "root"
win_states: "A C"
lose_states: "B D"
successors: """
root pacLeft minLeft
root pacRight minRight
minLeft gLeft A
minLeft gRight B
minRight gLeft C
minRight gRight deeper
deeper pacLeft D
"""
evaluation: """
A 4.0
B 3.0
C 2.0
D 1000.0
"""
homework_2/test_cases/q3/1-1-minmax.solution
# This is the solution file for test_cases/q3/1-1-minmax.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 dx"
homework_2/test_cases/q3/1-1-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -3.01
a - max
b - min
c - max
Note that the minimax value of b1 is -3.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -3.01
"""
homework_2/test_cases/q3/1-2-minmax.solution
# This is the solution file for test_cases/q3/1-2-minmax.test.
action: "Right"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 dx"
homework_2/test_cases/q3/1-2-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -2.99
a - max
b - min
c - max
Note that the minimax value of b1 is -3.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -2.99
"""
homework_2/test_cases/q3/1-3-minmax.solution
# This is the solution file for test_cases/q3/1-3-minmax.test.
action: "Left"
generated: "a b1 b2 c3 cx d5 d6 dx"
homework_2/test_cases/q3/1-3-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
4.01 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b2 is 4.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 4.01
"""
homework_2/test_cases/q3/1-4-minmax.solution
# This is the solution file for test_cases/q3/1-4-minmax.test.
action: "Right"
generated: "a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q3/1-4-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
3.99 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b2 is 4.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 3.99
"""
homework_2/test_cases/q3/1-5-minmax.solution
# This is the solution file for test_cases/q3/1-5-minmax.test.
action: "Right"
generated: "A B C D E F G Z a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q3/1-5-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
/ \ / \ / \ / \ |
A B C D E F G H Z
-3 13 5 9 10 3 -6 8 3.01
a - max
b - min
c - max
d - min
Note the minimax value of b1 is 3.
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
A -3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 3.0
G -6.0
H 8.0
Z 3.01
"""
homework_2/test_cases/q3/1-6-minmax.solution
# This is the solution file for test_cases/q3/1-6-minmax.test.
action: "Left"
generated: "A B C D E F G Z a b1 b2 c1 c2 cx d1 d2 d3 d4 dx"
homework_2/test_cases/q3/1-6-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
c1 c2 cx
/ \ / \ |
d1 d2 d3 d4 dx
/ \ / \ / \ / \ |
A B C D E F G H Z
-3 13 5 9 10 3 -6 8 2.99
a - max
b - min
c - max
d - min
Note the minimax value of b1 is 3.
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
A -3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 3.0
G -6.0
H 8.0
Z 2.99
"""
homework_2/test_cases/q3/1-7-minmax.solution
# This is the solution file for test_cases/q3/1-7-minmax.test.
action: "Left"
generated: "I J K M O P Z a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q3/1-7-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
| / \ / \ / \ / \
Z I J K L M N O P
-1.99 -1 -9 4 7 2 5 -3 -2
a - max
b - min
c - min
d - max
Note that the minimax value of b2 is -2
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
I -1.0
J -9.0
K 4.0
L 7.0
M 2.0
N 5.0
O -3.0
P -2.0
Z -1.99
"""
homework_2/test_cases/q3/1-8-minmax.solution
# This is the solution file for test_cases/q3/1-8-minmax.test.
action: "Right"
generated: "I J K M O P Z a b1 b2 c3 c4 cx d5 d6 d7 d8 dx"
homework_2/test_cases/q3/1-8-minmax.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
cx c3 c4
| / \ / \
dx d5 d6 d7 d8
| / \ / \ / \ / \
Z I J K L M N O P
-2.01 -1 -9 4 7 2 5 -3 -2
a - max
b - min
c - min
d - max
Note that the minimax value of b2 is -2.01
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P Z"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
dx Down Z
"""
evaluation: """
I -1.0
J -9.0
K 4.0
L 7.0
M 2.0
N 5.0
O -3.0
P -2.0
Z -2.01
"""
homework_2/test_cases/q3/2-1a-vary-depth.solution
# This is the solution file for test_cases/q3/2-1a-vary-depth.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx"
homework_2/test_cases/q3/2-1a-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -4.01
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -4.01
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -4.01
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -4.01
"""
homework_2/test_cases/q3/2-1b-vary-depth.solution
# This is the solution file for test_cases/q3/2-1b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 dx"
homework_2/test_cases/q3/2-1b-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -4.01
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -4.01
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -4.01
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -4.01
"""
homework_2/test_cases/q3/2-2a-vary-depth.solution
# This is the solution file for test_cases/q3/2-2a-vary-depth.test.
action: "Right"
generated: "a b1 b2 c1 c2 cx"
homework_2/test_cases/q3/2-2a-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -3.99
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -3.99
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -3.99
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -3.99
"""
homework_2/test_cases/q3/2-2b-vary-depth.solution
# This is the solution file for test_cases/q3/2-2b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c1 c2 cx d1 d2 d3 dx"
homework_2/test_cases/q3/2-2b-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ |
-4 c1 c2 9 cx -3.99
/ \ / \ |
d1 d2 d3 d4 dx
-3 -9 10 6 -3.99
a - max
b - min
c - max
Note that the minimax value of b1 is -3, but the depth=1 limited value is -4.
The values next to c1, c2, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Down cx
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
cx Down dx
"""
evaluation: """
c1 -4.0
c2 9.0
cx -3.99
d1 -3.0
d2 -9.0
d3 10.0
d4 6.0
dx -3.99
"""
homework_2/test_cases/q3/2-3a-vary-depth.solution
# This is the solution file for test_cases/q3/2-3a-vary-depth.test.
action: "Left"
generated: "a b1 b2 c3 c4 cx"
homework_2/test_cases/q3/2-3a-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
5.01 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
5.01 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 5.01
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 5.01
"""
homework_2/test_cases/q3/2-3b-vary-depth.solution
# This is the solution file for test_cases/q3/2-3b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c3 cx d5 d6 dx"
homework_2/test_cases/q3/2-3b-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
5.01 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
5.01 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 5.01
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 5.01
"""
homework_2/test_cases/q3/2-4a-vary-depth.solution
# This is the solution file for test_cases/q3/2-4a-vary-depth.test.
action: "Right"
generated: "a b1 b2 c3 c4 cx"
homework_2/test_cases/q3/2-4a-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "1"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
4.99 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
4.99 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 4.99
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 4.99
"""
homework_2/test_cases/q3/2-4b-vary-depth.solution
# This is the solution file for test_cases/q3/2-4b-vary-depth.test.
action: "Left"
generated: "a b1 b2 c3 cx d5 d6 dx"
homework_2/test_cases/q3/2-4b-vary-depth.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
/-----a------\
/ \
/ \
b1 b2
| / \
4.99 cx 8 c3 c4 5
| / \ / \
dx d5 d6 d7 d8
4.99 4 -7 0 5
a - max
b - min
c - max
Note that the minimax value of b1 is 4, but the depth=1 limited value is 5.
The values next to c3, c4, and cx are the values of the evaluation function, not
necessarily the correct minimax backup.
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8 dx"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Down cx
b2 Left c3
b2 Right c4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
cx Down dx
"""
evaluation: """
c3 8.0
c4 5.0
cx 4.99
d5 4.0
d6 -7.0
d7 0.0
d8 5.0
dx 4.99
"""
homework_2/test_cases/q3/2-one-ghost-3level.solution
# This is the solution file for test_cases/q3/2-one-ghost-3level.test.
action: "Left"
generated: "a b1 b2 c1 c2 c3 d1 d2 d3 d5 d6"
homework_2/test_cases/q3/2-one-ghost-3level.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
3 9 10 6 4 7 0 5
a - max
b - min
c - max
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
"""
evaluation: """
d1 3.0
d2 9.0
d3 10.0
d4 6.0
d5 4.0
d6 7.0
d7 0.0
d8 5.0
"""
homework_2/test_cases/q3/3-one-ghost-4level.solution
# This is the solution file for test_cases/q3/3-one-ghost-4level.test.
action: "Left"
generated: "A B C D E F I K a b1 b2 c1 c2 c3 d1 d2 d3 d5 d6"
homework_2/test_cases/q3/3-one-ghost-4level.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
/ \ / \ / \ / \ / \ / \ / \ / \
A B C D E F G H I J K L M N O P
3 13 5 9 10 11 6 8 1 0 4 7 12 15 2 14
a - max
b - min
c - max
d - min
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
"""
evaluation: """
A 3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 11.0
G 6.0
H 8.0
I 1.0
J 0.0
K 4.0
L 7.0
M 12.0
N 15.0
O 2.0
P 14.0
"""
homework_2/test_cases/q3/4-two-ghosts-3level.solution
# This is the solution file for test_cases/q3/4-two-ghosts-3level.test.
action: "Left"
generated: "a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7"
homework_2/test_cases/q3/4-two-ghosts-3level.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
3 9 10 6 4 7 0 5
a - max
b - min
c - min
"""
num_agents: "3"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
"""
evaluation: """
d1 3.0
d2 9.0
d3 10.0
d4 6.0
d5 4.0
d6 7.0
d7 0.0
d8 5.0
"""
homework_2/test_cases/q3/5-two-ghosts-4level.solution
# This is the solution file for test_cases/q3/5-two-ghosts-4level.test.
action: "Left"
generated: "A B C D E G H I J a b1 b2 c1 c2 c3 d1 d2 d3 d4 d5"
homework_2/test_cases/q3/5-two-ghosts-4level.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
/ \ / \ / \ / \ / \ / \ / \ / \
A B C D E F G H I J K L M N O P
3 13 5 9 10 11 6 8 1 0 4 7 12 15 2 14
a - max
b - min
c - min
d - max
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
"""
evaluation: """
A 3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 11.0
G 6.0
H 8.0
I 1.0
J 0.0
K 4.0
L 7.0
M 12.0
N 15.0
O 2.0
P 14.0
"""
homework_2/test_cases/q3/6-tied-root.solution
# This is the solution file for test_cases/q3/6-tied-root.test.
action: "Left"
generated: "A B C max min1 min2"
homework_2/test_cases/q3/6-tied-root.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
max
/ \
min1 min2
| / \
A B C
10 10 0
"""
num_agents: "2"
start_state: "max"
win_states: "A B"
lose_states: "C"
successors: """
max Left min1
max Right min2
min1 Down A
min2 Left B
min2 Right C
"""
evaluation: """
A 10.0
B 10.0
C 0.0
"""
homework_2/test_cases/q3/7-1a-check-depth-one-ghost.solution
# This is the solution file for test_cases/q3/7-1a-check-depth-one-ghost.test.
action: "Left"
generated: "a b1 b2 b3 c1 c2 c3"
homework_2/test_cases/q3/7-1a-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "1"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 1, the evaluation function is called at level c,
so Left should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q3/7-1b-check-depth-one-ghost.solution
# This is the solution file for test_cases/q3/7-1b-check-depth-one-ghost.test.
action: "Center"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3"
homework_2/test_cases/q3/7-1b-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 2, the evaluation function is called at level e,
so Center should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q3/7-1c-check-depth-one-ghost.solution
# This is the solution file for test_cases/q3/7-1c-check-depth-one-ghost.test.
action: "Right"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3"
homework_2/test_cases/q3/7-1c-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 3, the evaluation function is called at level g,
so Right should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q3/7-2a-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q3/7-2a-check-depth-two-ghosts.test.
action: "Left"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3"
homework_2/test_cases/q3/7-2a-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "1"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 1, the evaluation function is called at level d,
so Left should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q3/7-2b-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q3/7-2b-check-depth-two-ghosts.test.
action: "Center"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3"
homework_2/test_cases/q3/7-2b-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "2"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 2, the evaluation function is called at level g,
so Center should be returned. If your algorithm is returning
a different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q3/7-2c-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q3/7-2c-check-depth-two-ghosts.test.
action: "Right"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3 h1 h2 h3 i1 i2 i3 j1 j2 j3"
homework_2/test_cases/q3/7-2c-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "AlphaBetaAgent"
depth: "3"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 3, the evaluation function is called at level j,
so Right should be returned. If your algorithm is returning
a different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q3/8-pacman-game.solution
optimalActions: """
[[["West", "East"], 39], [["West", "East"], 27]]
[[["West"], 75], [["West"], 57]]
[[["West"], 95], [["West"], 77]]
[[["West", "North"], 72], [["West", "North"], 54]]
[[["West"], 51], [["West"], 39]]
[[["West", "North"], 76], [["West", "North"], 58]]
[[["West"], 69], [["West"], 51]]
[[["West"], 34], [["West"], 23]]
[[["North"], 55], [["North"], 41]]
[[["North"], 63], [["North"], 32]]
[[["North"], 87], [["North"], 43]]
[[["North"], 69], [["North"], 34]]
[[["Stop", "East"], 116], [["East"], 56]]
[[["East"], 110], [["East"], 52]]
[[["West"], 28], [["West"], 17]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["North"], 28], [["North"], 18]]
[[["North"], 47], [["North"], 30]]
[[["West"], 35], [["West"], 23]]
[[["East"], 28], [["East"], 15]]
[[["North"], 74], [["North"], 48]]
[[["East", "North"], 130], [["East", "North"], 87]]
[[["East", "North"], 213], [["East", "North"], 158]]
[[["North", "South"], 101], [["North", "South"], 73]]
[[["West"], 48], [["West"], 36]]
[[["West"], 60], [["West"], 45]]
[[["West", "Stop"], 49], [["West"], 36]]
[[["West", "Stop", "East"], 69], [["West", "East"], 49]]
[[["West", "Stop", "East"], 61], [["West", "East"], 41]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 28], [["East", "South"], 19]]
[[["Stop", "East", "South"], 34], [["East", "South"], 23]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 81], [["East", "South"], 53]]
[[["Stop", "East", "South"], 64], [["East", "South"], 43]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 81], [["East", "South"], 53]]
[[["Stop", "East", "South"], 102], [["East", "South"], 67]]
[[["Stop", "South"], 23], [["South"], 13]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East", "North"], 28], [["East", "North"], 18]]
[[["East"], 35], [["East"], 22]]
[[["North"], 28], [["North"], 18]]
[[["North"], 38], [["North"], 22]]
[[["East"], 30], [["East"], 20]]
[[["East"], 33], [["East"], 18]]
[[["East"], 18], [["East"], 12]]
[[["East"], 33], [["East"], 23]]
[[["East"], 48], [["East"], 29]]
[[["East"], 49], [["East"], 26]]
[[["East"], 44], [["East"], 29]]
[[["North", "South"], 75], [["North", "South"], 52]]
[[["East", "North"], 113], [["East", "North"], 74]]
[[["East", "North"], 89], [["East", "North"], 65]]
[[["North", "South"], 173], [["North", "South"], 130]]
[[["West", "East"], 54], [["West", "East"], 42]]
[[["West", "Stop", "East"], 130], [["West", "East"], 87]]
[[["Stop", "East", "South"], 58], [["East", "South"], 43]]
[[["Stop", "East"], 110], [["South"], 82]]
[[["East"], 71], [["East"], 45]]
[[["West"], 66], [["West"], 42]]
[[["South"], 70], [["South"], 48]]
[[["South"], 48], [["South"], 37]]
[[["South"], 179], [["South"], 104]]
[[["South"], 68], [["South"], 41]]
[[["West"], 25], [["West"], 18]]
[[["West"], 41], [["West"], 29]]
[[["West"], 14], [["West"], 10]]
[[["West"], 20], [["West"], 14]]
[[["West"], 13], [["West"], 9]]
[[["West"], 13], [["West"], 9]]
[[["West"], 16], [["West"], 12]]
[[["West", "North"], 28], [["West", "North"], 20]]
[[["West"], 30], [["West"], 23]]
[[["West", "Stop", "East", "North"], 66], [["West", "East", "North"], 46]]
[[["West", "Stop", "East"], 126], [["West", "East"], 89]]
[[["West", "Stop", "East"], 30], [["West", "East"], 20]]
[[["Stop", "East", "North"], 67], [["East", "North"], 45]]
[[["Stop", "North"], 52], [["North"], 27]]
[[["North"], 30], [["North"], 19]]
[[["North"], 28], [["North"], 17]]
[[["North"], 17], [["North"], 11]]
[[["North"], 30], [["North"], 19]]
[[["East"], 28], [["East"], 17]]
[[["East"], 17], [["East"], 11]]
[[["East"], 40], [["East"], 29]]
[[["East", "South"], 79], [["East", "South"], 60]]
[[["East", "South"], 90], [["East", "South"], 62]]
[[["South"], 96], [["South"], 61]]
[[["North", "South"], 195], [["North", "South"], 132]]
[[["West"], 80], [["West"], 60]]
[[["West", "Stop", "East", "South"], 83], [["West", "East", "South"], 61]]
[[["West", "Stop", "East", "South"], 90], [["West", "East", "South"], 66]]
[[["West", "Stop", "East"], 134], [["West", "East"], 95]]
[[["West", "Stop", "East"], 82], [["West", "East"], 55]]
[[["Stop", "East", "South"], 142], [["East", "South"], 95]]
[[["Stop", "East", "South"], 95], [["East", "South"], 62]]
[[["Stop", "East", "South"], 128], [["East", "South"], 86]]
[[["Stop", "East", "South"], 82], [["East", "South"], 55]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 182], [["East", "South"], 119]]
[[["Stop", "East", "South"], 154], [["East", "South"], 103]]
[[["Stop", "South"], 46], [["South"], 25]]
[[["South"], 28], [["South"], 17]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East", "North"], 28], [["East", "North"], 18]]
[[["East"], 37], [["East"], 22]]
[[["East", "North"], 34], [["East", "North"], 24]]
[[["East"], 56], [["East"], 29]]
[[["East"], 17], [["East"], 11]]
[[["East"], 26], [["East"], 15]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East"], 27], [["East"], 17]]
[[["East"], 37], [["East"], 22]]
[[["East", "North"], 37], [["East", "North"], 24]]
[[["East"], 56], [["East"], 29]]
[[["East"], 19], [["East"], 11]]
[[["North"], 26], [["North"], 15]]
[[["North"], 19], [["North"], 11]]
[[["North"], 30], [["North"], 17]]
[[["North"], 30], [["North"], 19]]
[[["West"], 28], [["West"], 19]]
[[["West"], 23], [["West"], 13]]
[[["West", "Stop", "East"], 7], [["West", "East"], 3]]
"""
altDepthActions: """
[["West", "East"], ["West", "East"], ["West", "East"], ["West", "East"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["Stop", "North"], ["North"]]
[["East"], ["East"], ["Stop", "East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["West"], ["West"], ["West"], ["West"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["West"], ["West"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"], ["North"], ["North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West", "Stop"], ["West"]]
[["West"], ["West"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"], ["Stop", "South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North", "South"], ["North", "South"], ["South"], ["South"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"], ["North"], ["North"]]
[["West", "East"], ["West", "East"], ["East"], ["East"]]
[["West"], ["West"], ["East"], ["East"]]
[["Stop", "East", "South"], ["East", "South"], ["East"], ["East"]]
[["Stop", "East"], ["East"], ["Stop", "East"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["West"], ["West"], ["West"], ["West"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["West", "East"], ["West", "East"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East", "North"], ["West", "East", "North"], ["West", "Stop", "East", "North"], ["West", "East", "North"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "North"], ["East", "North"], ["Stop", "East", "North"], ["East", "North"]]
[["Stop", "North"], ["North"], ["Stop", "North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "South"], ["East", "South"], ["East", "South"], ["East", "South"]]
[["East", "South"], ["East", "South"], ["East", "South"], ["East", "South"]]
[["South"], ["South"], ["South"], ["South"]]
[["North", "South"], ["North", "South"], ["North", "South"], ["North", "South"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"], ["Stop", "South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
"""
partialPlyBugActions: """
[["West", "East"], ["West", "East"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["Stop", "East"], ["East"]]
[["West"], ["West"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["West"], ["West"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "Stop"], ["West"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North", "South"], ["North", "South"]]
[["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"]]
[["West", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East"], ["East"]]
[["East"], ["East"]]
[["West"], ["West"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West", "Stop", "East", "North"], ["West", "East", "North"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "North"], ["East", "North"]]
[["Stop", "North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "South"], ["East", "South"]]
[["East", "South"], ["East", "South"]]
[["South"], ["South"]]
[["North", "South"], ["North", "South"]]
[["West"], ["West"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "Stop", "East"], ["West", "East"]]
"""
homework_2/test_cases/q3/8-pacman-game.test
class: "PacmanGameTreeTest"
alg: "AlphaBetaAgent"
seed: "0"
depth: "2"
max_points: "4"
# The following specifies the layout to be used
layoutName: "smallClassic"
layout: """
%%%%%%%%%%%%%%%%%%%%
%......%G G%......%
%.%%...%% %%...%%.%
%.%o.%........%.o%.%
%.%%.%.%%%%%%.%.%%.%
%........P.........%
%%%%%%%%%%%%%%%%%%%%
"""
homework_2/test_cases/q3/CONFIG
max_points: "5"
class: "PassAllTestsQuestion"
homework_2/test_cases/q4/0-eval-function-lose-states-1.solution
# This is the solution file for test_cases/q4/0-eval-function-lose-states-1.test.
action: "Left"
generated: "lose1 lose2 root"
homework_2/test_cases/q4/0-eval-function-lose-states-1.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
diagram: """
root
/ \
lose1 lose2
1 0
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on losing states.
"""
num_agents: "2"
start_state: "root"
win_states: ""
lose_states: "lose1 lose2"
successors: """
root Left lose1
root Right lose2
"""
evaluation: """
lose1 1.0
lose2 0.0
"""
homework_2/test_cases/q4/0-eval-function-lose-states-2.solution
# This is the solution file for test_cases/q4/0-eval-function-lose-states-2.test.
action: "Right"
generated: "lose1 lose2 root"
homework_2/test_cases/q4/0-eval-function-lose-states-2.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
diagram: """
root
/ \
lose1 lose2
0 1
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on losing states.
"""
num_agents: "2"
start_state: "root"
win_states: ""
lose_states: "lose1 lose2"
successors: """
root Left lose1
root Right lose2
"""
evaluation: """
lose1 0.0
lose2 1.0
"""
homework_2/test_cases/q4/0-eval-function-win-states-1.solution
# This is the solution file for test_cases/q4/0-eval-function-win-states-1.test.
action: "Left"
generated: "root win1 win2"
homework_2/test_cases/q4/0-eval-function-win-states-1.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
diagram: """
root
/ \
win1 win2
1 0
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on winning states.
"""
num_agents: "2"
start_state: "root"
win_states: "win1 win2"
lose_states: ""
successors: """
root Left win1
root Right win2
"""
evaluation: """
win1 1.0
win2 0.0
"""
homework_2/test_cases/q4/0-eval-function-win-states-2.solution
# This is the solution file for test_cases/q4/0-eval-function-win-states-2.test.
action: "Right"
generated: "root win1 win2"
homework_2/test_cases/q4/0-eval-function-win-states-2.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
diagram: """
root
/ \
win1 win2
0 1
If your algorithm is returning a different
action, make sure you are calling the
evaluation function on winning states.
"""
num_agents: "2"
start_state: "root"
win_states: "win1 win2"
lose_states: ""
successors: """
root Left win1
root Right win2
"""
evaluation: """
win1 0.0
win2 1.0
"""
homework_2/test_cases/q4/0-expectimax1.solution
# This is the solution file for test_cases/q4/0-expectimax1.test.
action: "Left"
generated: "A B C D E F G H I exp1 exp2 exp3 max"
homework_2/test_cases/q4/0-expectimax1.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
# Tree adapted from lecture 6 slides
diagram: """
max
/-/ | \--\
/ | \
/ | \
exp1 exp2 exp3
/|\ /|\ /|\
/ | \ / | \ / | \
A B C D E F G H I
3 12 8 2 4 6 14 5 2
"""
num_agents: "2"
start_state: "max"
win_states: "A B C D E F G H I"
lose_states: ""
successors: """
max Left exp1
max Center exp2
max Right exp3
exp1 Left A
exp1 Center B
exp1 Right C
exp2 Left D
exp2 Center E
exp2 Right F
exp3 Left G
exp3 Center H
exp3 Right I
"""
evaluation: """
A 3.0
B 12.0
C 8.0
D 2.0
E 4.0
F 6.0
G 14.0
H 5.0
I 2.0
"""
homework_2/test_cases/q4/1-expectimax2.solution
# This is the solution file for test_cases/q4/1-expectimax2.test.
action: "Left"
generated: "A B C D E F G H I exp1 exp2 exp3 max"
homework_2/test_cases/q4/1-expectimax2.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
# Tree from lecture 7 slides
diagram: """
max
/-/ | \--\
/ | \
/ | \
exp1 exp2 exp3
/|\ /|\ /|\
/ | \ / | \ / | \
A B C D E F G H I
3 12 9 2 4 6 15 6 0
"""
num_agents: "2"
start_state: "max"
win_states: "A B C D E F G H I"
lose_states: ""
successors: """
max Left exp1
max Center exp2
max Right exp3
exp1 Left A
exp1 Center B
exp1 Right C
exp2 Left D
exp2 Center E
exp2 Right F
exp3 Left G
exp3 Center H
exp3 Right I
"""
evaluation: """
A 3.0
B 12.0
C 9.0
D 2.0
E 4.0
F 6.0
G 15.0
H 6.0
I 0.0
"""
homework_2/test_cases/q4/2-one-ghost-3level.solution
# This is the solution file for test_cases/q4/2-one-ghost-3level.test.
action: "Left"
generated: "a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q4/2-one-ghost-3level.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
3 9 10 6 4 7 0 5
a - max
b - exp
c - max
"""
num_agents: "2"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
"""
evaluation: """
d1 3.0
d2 9.0
d3 10.0
d4 6.0
d5 4.0
d6 7.0
d7 0.0
d8 5.0
"""
homework_2/test_cases/q4/3-one-ghost-4level.solution
# This is the solution file for test_cases/q4/3-one-ghost-4level.test.
action: "Right"
generated: "A B C D E F G H I J K L M N O P a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q4/3-one-ghost-4level.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
/ \ / \ / \ / \ / \ / \ / \ / \
A B C D E F G H I J K L M N O P
3 13 5 9 10 11 6 8 1 0 4 7 12 15 2 14
a - max
b - exp
c - max
d - exp
"""
num_agents: "2"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
"""
evaluation: """
A 3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 11.0
G 6.0
H 8.0
I 1.0
J 0.0
K 4.0
L 7.0
M 12.0
N 15.0
O 2.0
P 14.0
"""
homework_2/test_cases/q4/4-two-ghosts-3level.solution
# This is the solution file for test_cases/q4/4-two-ghosts-3level.test.
action: "Left"
generated: "a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q4/4-two-ghosts-3level.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "3"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
3 9 10 6 4 7 0 5
a - max
b - exp
c - exp
"""
num_agents: "3"
start_state: "a"
win_states: "d1 d2 d3 d4 d5 d6 d7 d8"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
"""
evaluation: """
d1 3.0
d2 9.0
d3 10.0
d4 6.0
d5 4.0
d6 7.0
d7 0.0
d8 5.0
"""
homework_2/test_cases/q4/5-two-ghosts-4level.solution
# This is the solution file for test_cases/q4/5-two-ghosts-4level.test.
action: "Left"
generated: "A B C D E F G H I J K L M N O P a b1 b2 c1 c2 c3 c4 d1 d2 d3 d4 d5 d6 d7 d8"
homework_2/test_cases/q4/5-two-ghosts-4level.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "4"
diagram: """
/-----a------\
/ \
/ \
b1 b2
/ \ / \
c1 c2 c3 c4
/ \ / \ / \ / \
d1 d2 d3 d4 d5 d6 d7 d8
/ \ / \ / \ / \ / \ / \ / \ / \
A B C D E F G H I J K L M N O P
3 13 5 9 10 11 6 8 1 0 4 7 12 15 2 14
a - max
b - exp
c - exp
d - max
"""
num_agents: "3"
start_state: "a"
win_states: "A B C D E F G H I J K L M N O P"
lose_states: ""
successors: """
a Left b1
a Right b2
b1 Left c1
b1 Right c2
b2 Left c3
b2 Right c4
c1 Left d1
c1 Right d2
c2 Left d3
c2 Right d4
c3 Left d5
c3 Right d6
c4 Left d7
c4 Right d8
d1 Left A
d1 Right B
d2 Left C
d2 Right D
d3 Left E
d3 Right F
d4 Left G
d4 Right H
d5 Left I
d5 Right J
d6 Left K
d6 Right L
d7 Left M
d7 Right N
d8 Left O
d8 Right P
"""
evaluation: """
A 3.0
B 13.0
C 5.0
D 9.0
E 10.0
F 11.0
G 6.0
H 8.0
I 1.0
J 0.0
K 4.0
L 7.0
M 12.0
N 15.0
O 2.0
P 14.0
"""
homework_2/test_cases/q4/6-1a-check-depth-one-ghost.solution
# This is the solution file for test_cases/q4/6-1a-check-depth-one-ghost.test.
action: "Left"
generated: "a b1 b2 b3 c1 c2 c3"
homework_2/test_cases/q4/6-1a-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "1"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 1, the evaluation function is called at level c,
so Left should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q4/6-1b-check-depth-one-ghost.solution
# This is the solution file for test_cases/q4/6-1b-check-depth-one-ghost.test.
action: "Center"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3"
homework_2/test_cases/q4/6-1b-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 2, the evaluation function is called at level e,
so Center should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q4/6-1c-check-depth-one-ghost.solution
# This is the solution file for test_cases/q4/6-1c-check-depth-one-ghost.test.
action: "Right"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3"
homework_2/test_cases/q4/6-1c-check-depth-one-ghost.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "3"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
10 c1 0 c2 c3 8
| | |
0 d1 0 d2 d3 8
| | |
0 e1 10 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
g1 g2 g3
0 0 8
a - max
b - min
c - max
d - min
e - max
f - min
At depth 3, the evaluation function is called at level g,
so Right should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "2"
start_state: "a"
win_states: "g1 g2 g3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 10.0
c2 0.0
c3 8.0
d1 0.0
d2 0.0
d3 8.0
e1 0.0
e2 10.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 0.0
g3 8.0
"""
homework_2/test_cases/q4/6-2a-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q4/6-2a-check-depth-two-ghosts.test.
action: "Left"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3"
homework_2/test_cases/q4/6-2a-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "1"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 1, the evaluation function is called at level d,
so Left should be returned. If your algorithm is returning a
different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q4/6-2b-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q4/6-2b-check-depth-two-ghosts.test.
action: "Center"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3"
homework_2/test_cases/q4/6-2b-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "2"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 2, the evaluation function is called at level g,
so Center should be returned. If your algorithm is returning
a different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q4/6-2c-check-depth-two-ghosts.solution
# This is the solution file for test_cases/q4/6-2c-check-depth-two-ghosts.test.
action: "Right"
generated: "a b1 b2 b3 c1 c2 c3 d1 d2 d3 e1 e2 e3 f1 f2 f3 g1 g2 g3 h1 h2 h3 i1 i2 i3 j1 j2 j3"
homework_2/test_cases/q4/6-2c-check-depth-two-ghosts.test
class: "GraphGameTreeTest"
alg: "ExpectimaxAgent"
depth: "3"
diagram: """
a
/-/ | \--\
/ | \
0 b1 0 b2 b3 8
| | |
0 c1 0 c2 c3 8
| | |
10 d1 0 d2 d3 8
| | |
0 e1 0 e2 e3 8
| | |
0 f1 0 f2 f3 8
| | |
0 g1 10 g2 g3 8
| | |
0 h1 0 h2 h3 8
| | |
0 i1 0 i2 i3 8
| | |
j1 j2 j3
0 0 8
a - max
b - min
c - min
d - max
e - min
f - min
g - max
h - min
i - min
At depth 3, the evaluation function is called at level j,
so Right should be returned. If your algorithm is returning
a different action, check how you implemented your depth.
"""
num_agents: "3"
start_state: "a"
win_states: "j1 j2 j3"
lose_states: ""
successors: """
a Left b1
a Center b2
a Right b3
b1 Center c1
b2 Center c2
b3 Center c3
c1 Center d1
c2 Center d2
c3 Center d3
d1 Center e1
d2 Center e2
d3 Center e3
e1 Center f1
e2 Center f2
e3 Center f3
f1 Center g1
f2 Center g2
f3 Center g3
g1 Center h1
g2 Center h2
g3 Center h3
h1 Center i1
h2 Center i2
h3 Center i3
i1 Center j1
i2 Center j2
i3 Center j3
"""
evaluation: """
b1 0.0
b2 0.0
b3 8.0
c1 0.0
c2 0.0
c3 8.0
d1 10.0
d2 0.0
d3 8.0
e1 0.0
e2 0.0
e3 8.0
f1 0.0
f2 0.0
f3 8.0
g1 0.0
g2 10.0
g3 8.0
h1 0.0
h2 0.0
h3 8.0
i1 0.0
i2 0.0
i3 8.0
j1 0.0
j2 0.0
j3 8.0
"""
homework_2/test_cases/q4/7-pacman-game.solution
optimalActions: """
[[["West", "East"], 59], [["West", "East"], 35]]
[[["West"], 190], [["West"], 127]]
[[["West"], 190], [["West"], 135]]
[[["West", "North"], 120], [["West", "North"], 82]]
[[["West"], 77], [["West"], 57]]
[[["West", "North"], 143], [["West", "North"], 97]]
[[["West"], 155], [["West"], 110]]
[[["West"], 40], [["West"], 27]]
[[["North"], 64], [["North"], 43]]
[[["North"], 85], [["North"], 57]]
[[["North"], 106], [["North"], 71]]
[[["North"], 97], [["North"], 65]]
[[["East"], 154], [["East"], 103]]
[[["Stop"], 130], [["West"], 85]]
[[["West"], 30], [["West"], 17]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["North"], 29], [["North"], 18]]
[[["North"], 50], [["North"], 31]]
[[["West"], 55], [["West"], 36]]
[[["East"], 29], [["East"], 16]]
[[["North"], 89], [["North"], 61]]
[[["East", "North"], 161], [["East", "North"], 121]]
[[["East", "North"], 221], [["East", "North"], 166]]
[[["North", "South"], 105], [["North", "South"], 77]]
[[["West"], 69], [["West"], 51]]
[[["West"], 94], [["West"], 69]]
[[["West", "Stop"], 57], [["West"], 42]]
[[["West", "Stop", "East"], 69], [["West", "East"], 49]]
[[["West", "Stop", "East"], 61], [["West", "East"], 41]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 28], [["East", "South"], 19]]
[[["Stop", "East", "South"], 34], [["East", "South"], 23]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 55], [["East", "South"], 37]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 64], [["East", "South"], 43]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 61], [["East", "South"], 41]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 102], [["East", "South"], 67]]
[[["Stop", "South"], 23], [["South"], 13]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East", "North"], 29], [["East", "North"], 18]]
[[["East"], 38], [["East"], 22]]
[[["North"], 29], [["North"], 18]]
[[["North"], 38], [["North"], 22]]
[[["East"], 33], [["East"], 22]]
[[["East"], 37], [["East"], 18]]
[[["East"], 18], [["East"], 12]]
[[["East"], 37], [["East"], 26]]
[[["East"], 69], [["East"], 41]]
[[["East"], 56], [["East"], 26]]
[[["East"], 44], [["East"], 29]]
[[["North", "South"], 83], [["North", "South"], 52]]
[[["East", "North"], 121], [["East", "North"], 74]]
[[["East", "North"], 97], [["East", "North"], 73]]
[[["North", "South"], 173], [["North", "South"], 130]]
[[["West", "East"], 90], [["West", "East"], 66]]
[[["West", "Stop", "East"], 161], [["West", "East"], 118]]
[[["Stop", "East", "South"], 58], [["East", "South"], 43]]
[[["Stop", "East"], 120], [["East"], 85]]
[[["East"], 78], [["East"], 45]]
[[["West"], 77], [["West"], 42]]
[[["South"], 83], [["South"], 48]]
[[["South"], 49], [["South"], 37]]
[[["South"], 185], [["South"], 104]]
[[["South"], 68], [["South"], 41]]
[[["West"], 30], [["West"], 18]]
[[["West"], 56], [["West"], 29]]
[[["West"], 14], [["West"], 10]]
[[["West"], 20], [["West"], 14]]
[[["West"], 13], [["West"], 9]]
[[["West"], 13], [["West"], 9]]
[[["West"], 16], [["West"], 12]]
[[["West", "North"], 30], [["West", "North"], 20]]
[[["West"], 38], [["West"], 23]]
[[["West", "Stop", "East", "North"], 70], [["West", "East", "North"], 46]]
[[["West", "Stop", "East"], 128], [["West", "East"], 89]]
[[["West", "Stop", "East"], 31], [["West", "East"], 20]]
[[["Stop", "East", "North"], 69], [["East", "North"], 45]]
[[["Stop", "North"], 58], [["North"], 31]]
[[["North"], 34], [["North"], 19]]
[[["North"], 30], [["North"], 17]]
[[["North"], 19], [["North"], 11]]
[[["North"], 34], [["North"], 19]]
[[["East"], 30], [["East"], 17]]
[[["East"], 19], [["East"], 11]]
[[["East"], 44], [["East"], 29]]
[[["East", "South"], 87], [["East", "South"], 60]]
[[["East", "South"], 108], [["East", "South"], 62]]
[[["South"], 120], [["South"], 61]]
[[["North", "South"], 209], [["North", "South"], 132]]
[[["West"], 108], [["West"], 60]]
[[["West", "Stop", "East", "South"], 83], [["West", "East", "South"], 61]]
[[["West", "Stop", "East", "South"], 90], [["West", "East", "South"], 66]]
[[["West", "Stop", "East"], 134], [["West", "East"], 95]]
[[["West", "Stop", "East"], 82], [["West", "East"], 55]]
[[["Stop", "East", "South"], 142], [["East", "South"], 95]]
[[["Stop", "East", "South"], 98], [["East", "South"], 65]]
[[["Stop", "East", "South"], 128], [["East", "South"], 86]]
[[["Stop", "East", "South"], 82], [["East", "South"], 55]]
[[["Stop", "East", "South"], 85], [["East", "South"], 57]]
[[["Stop", "East", "South"], 190], [["East", "South"], 127]]
[[["Stop", "East", "South"], 158], [["East", "South"], 103]]
[[["Stop", "South"], 50], [["South"], 27]]
[[["South"], 30], [["South"], 17]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["South"], 15], [["South"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East", "North"], 29], [["East", "North"], 18]]
[[["East"], 37], [["East"], 22]]
[[["East", "North"], 41], [["East", "North"], 24]]
[[["East"], 59], [["East"], 29]]
[[["East"], 19], [["East"], 11]]
[[["East"], 26], [["East"], 15]]
[[["East"], 15], [["East"], 9]]
[[["East"], 15], [["East"], 9]]
[[["East"], 18], [["East"], 12]]
[[["East"], 29], [["East"], 18]]
[[["East"], 37], [["East"], 22]]
[[["East", "North"], 41], [["East", "North"], 24]]
[[["East"], 59], [["East"], 29]]
[[["East"], 19], [["East"], 11]]
[[["North"], 26], [["North"], 15]]
[[["North"], 19], [["North"], 11]]
[[["North"], 30], [["North"], 17]]
[[["North"], 34], [["North"], 19]]
[[["West"], 34], [["West"], 19]]
[[["West"], 25], [["West"], 13]]
[[["West", "Stop", "East"], 7], [["West", "East"], 3]]
"""
altDepthActions: """
[["West", "East"], ["West", "East"], ["West", "East"], ["West", "East"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["West", "Stop"], ["West"], ["Stop"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["West"], ["West"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["North", "South"], ["North", "South"], ["North"], ["North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West", "Stop"], ["West"]]
[["West"], ["West"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"], ["Stop", "South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North", "South"], ["North", "South"], ["South"], ["South"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"], ["North"], ["North"]]
[["West", "East"], ["West", "East"], ["East"], ["East"]]
[["West"], ["West"], ["East"], ["East"]]
[["Stop", "East", "South"], ["East", "South"], ["East"], ["East"]]
[["Stop", "East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["West"], ["West"], ["West"], ["West"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["West", "East"], ["West", "East"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "North"], ["West", "North"], ["West", "North"], ["West", "North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East", "North"], ["West", "East", "North"], ["West", "Stop", "East", "North"], ["West", "East", "North"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "North"], ["East", "North"], ["Stop", "East", "North"], ["East", "North"]]
[["Stop", "North"], ["North"], ["Stop", "North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "South"], ["East", "South"], ["East", "South"], ["East", "South"]]
[["East", "South"], ["East", "South"], ["East", "South"], ["East", "South"]]
[["South"], ["South"], ["South"], ["South"]]
[["North", "South"], ["North", "South"], ["North", "South"], ["North", "South"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"], ["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"], ["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"], ["Stop", "South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["South"], ["South"], ["South"], ["South"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East", "North"], ["East", "North"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East", "North"], ["East", "North"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["East"], ["East"], ["East"], ["East"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["North"], ["North"], ["North"], ["North"]]
[["West"], ["West"], ["West"], ["West"]]
[["West"], ["West"], ["West"], ["West"]]
[["West", "Stop", "East"], ["West", "East"], ["West", "Stop", "East"], ["West", "East"]]
"""
partialPlyBugActions: """
[["West", "East"], ["West", "East"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["Stop"], ["West"]]
[["West"], ["West"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["West"], ["West"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "Stop"], ["West"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North", "South"], ["North", "South"]]
[["East", "North"], ["East", "North"]]
[["East", "North"], ["East", "North"]]
[["North", "South"], ["North", "South"]]
[["West", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East"], ["East"]]
[["East"], ["East"]]
[["West"], ["West"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "North"], ["West", "North"]]
[["West"], ["West"]]
[["West", "Stop", "East", "North"], ["West", "East", "North"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "North"], ["East", "North"]]
[["Stop", "North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "South"], ["East", "South"]]
[["East", "South"], ["East", "South"]]
[["South"], ["South"]]
[["North", "South"], ["North", "South"]]
[["West"], ["West"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East", "South"], ["West", "East", "South"]]
[["West", "Stop", "East"], ["West", "East"]]
[["West", "Stop", "East"], ["West", "East"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "East", "South"], ["East", "South"]]
[["Stop", "South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["South"], ["South"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["East", "North"], ["East", "North"]]
[["East"], ["East"]]
[["East"], ["East"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["North"], ["North"]]
[["West"], ["West"]]
[["West"], ["West"]]
[["West", "Stop", "East"], ["West", "East"]]
"""
homework_2/test_cases/q4/7-pacman-game.test
class: "PacmanGameTreeTest"
alg: "ExpectimaxAgent"
seed: "0"
depth: "2"
max_points: "4"
# The following specifies the layout to be used
layoutName: "smallClassic"
layout: """
%%%%%%%%%%%%%%%%%%%%
%......%G G%......%
%.%%...%% %%...%%.%
%.%o.%........%.o%.%
%.%%.%.%%%%%%.%.%%.%
%........P.........%
%%%%%%%%%%%%%%%%%%%%
"""
homework_2/test_cases/q4/CONFIG
max_points: "5"
class: "PassAllTestsQuestion"
homework_2/test_cases/q5/CONFIG
max_points: "6"
class: "PartialCreditQuestion"
homework_2/test_cases/q5/grade-agent.solution
# This is the solution file for test_cases/q5/grade-agent.test.
# File intentionally blank.
homework_2/test_cases/q5/grade-agent.test
class: "EvalAgentTest"
agentName: "ExpectimaxAgent"
agentArgs: "evalFn=better"
layoutName: "smallClassic"
maxTime: "120"
numGames: "10"
nonTimeoutMinimum: "0"
nonTimeoutThresholds: "10"
scoreThresholds: "500 1000"
winsMinimum: "1"
winsThresholds: "1 5 10"
randomSeed: "0"
ghosts: "[RandomGhost(1)]"
homework_2/textDisplay.py
# textDisplay.py
# --------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
import time
try:
import pacman
except:
pass
DRAW_EVERY = 1
SLEEP_TIME = 0 # This can be overwritten by __init__
DISPLAY_MOVES = False
QUIET = False # Supresses output
class NullGraphics:
def initialize(self, state, isBlue=False):
pass
def update(self, state):
pass
def checkNullDisplay(self):
return True
def pause(self):
time.sleep(SLEEP_TIME)
def draw(self, state):
print(state)
def updateDistributions(self, dist):
pass
def finish(self):
pass
class PacmanGraphics:
def __init__(self, speed=None):
if speed != None:
global SLEEP_TIME
SLEEP_TIME = speed
def initialize(self, state, isBlue=False):
self.draw(state)
self.pause()
self.turn = 0
self.agentCounter = 0
def update(self, state):
numAgents = len(state.agentStates)
self.agentCounter = (self.agentCounter + 1) % numAgents
if self.agentCounter == 0:
self.turn += 1
if DISPLAY_MOVES:
ghosts = [pacman.nearestPoint(
state.getGhostPosition(i)) for i in range(1, numAgents)]
print("%4d) P: %-8s" % (self.turn, str(pacman.nearestPoint(state.getPacmanPosition()))),
'| Score: %-5d' % state.score, '| Ghosts:', ghosts)
if self.turn % DRAW_EVERY == 0:
self.draw(state)
self.pause()
if state._win or state._lose:
self.draw(state)
def pause(self):
time.sleep(SLEEP_TIME)
def draw(self, state):
print(state)
def finish(self):
pass
homework_2/util.py
# util.py
# -------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
# util.py
# -------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
import sys
import inspect
import heapq
import random
import io
class FixedRandom:
def __init__(self):
fixedState = (3, (2147483648, 507801126, 683453281, 310439348, 2597246090,
2209084787, 2267831527, 979920060, 3098657677, 37650879, 807947081, 3974896263,
881243242, 3100634921, 1334775171, 3965168385, 746264660, 4074750168, 500078808,
776561771, 702988163, 1636311725, 2559226045, 157578202, 2498342920, 2794591496,
4130598723, 496985844, 2944563015, 3731321600, 3514814613, 3362575829, 3038768745,
2206497038, 1108748846, 1317460727, 3134077628, 988312410, 1674063516, 746456451,
3958482413, 1857117812, 708750586, 1583423339, 3466495450, 1536929345, 1137240525,
3875025632, 2466137587, 1235845595, 4214575620, 3792516855, 657994358, 1241843248,
1695651859, 3678946666, 1929922113, 2351044952, 2317810202, 2039319015, 460787996,
3654096216, 4068721415, 1814163703, 2904112444, 1386111013, 574629867, 2654529343,
3833135042, 2725328455, 552431551, 4006991378, 1331562057, 3710134542, 303171486,
1203231078, 2670768975, 54570816, 2679609001, 578983064, 1271454725, 3230871056,
2496832891, 2944938195, 1608828728, 367886575, 2544708204, 103775539, 1912402393,
1098482180, 2738577070, 3091646463, 1505274463, 2079416566, 659100352, 839995305,
1696257633, 274389836, 3973303017, 671127655, 1061109122, 517486945, 1379749962,
3421383928, 3116950429, 2165882425, 2346928266, 2892678711, 2936066049, 1316407868,
2873411858, 4279682888, 2744351923, 3290373816, 1014377279, 955200944, 4220990860,
2386098930, 1772997650, 3757346974, 1621616438, 2877097197, 442116595, 2010480266,
2867861469, 2955352695, 605335967, 2222936009, 2067554933, 4129906358, 1519608541,
1195006590, 1942991038, 2736562236, 279162408, 1415982909, 4099901426, 1732201505,
2934657937, 860563237, 2479235483, 3081651097, 2244720867, 3112631622, 1636991639,
3860393305, 2312061927, 48780114, 1149090394, 2643246550, 1764050647, 3836789087,
3474859076, 4237194338, 1735191073, 2150369208, 92164394, 756974036, 2314453957,
323969533, 4267621035, 283649842, 810004843, 727855536, 1757827251, 3334960421,
3261035106, 38417393, 2660980472, 1256633965, 2184045390, 811213141, 2857482069,
2237770878, 3891003138, 2787806886, 2435192790, 2249324662, 3507764896, 995388363,
856944153, 619213904, 3233967826, 3703465555, 3286531781, 3863193356, 2992340714,
413696855, 3865185632, 1704163171, 3043634452, 2225424707, 2199018022, 3506117517,
3311559776, 3374443561, 1207829628, 668793165, 1822020716, 2082656160, 1160606415,
3034757648, 741703672, 3094328738, 459332691, 2702383376, 1610239915, 4162939394,
557861574, 3805706338, 3832520705, 1248934879, 3250424034, 892335058, 74323433,
3209751608, 3213220797, 3444035873, 3743886725, 1783837251, 610968664, 580745246,
4041979504, 201684874, 2673219253, 1377283008, 3497299167, 2344209394, 2304982920,
3081403782, 2599256854, 3184475235, 3373055826, 695186388, 2423332338, 222864327,
1258227992, 3627871647, 3487724980, 4027953808, 3053320360, 533627073, 3026232514,
2340271949, 867277230, 868513116, 2158535651, 2487822909, 3428235761, 3067196046,
3435119657, 1908441839, 788668797, 3367703138, 3317763187, 908264443, 2252100381,
764223334, 4127108988, 384641349, 3377374722, 1263833251, 1958694944, 3847832657,
1253909612, 1096494446, 555725445, 2277045895, 3340096504, 1383318686, 4234428127,
1072582179, 94169494, 1064509968, 2681151917, 2681864920, 734708852, 1338914021,
1270409500, 1789469116, 4191988204, 1716329784, 2213764829, 3712538840, 919910444,
1318414447, 3383806712, 3054941722, 3378649942, 1205735655, 1268136494, 2214009444,
2532395133, 3232230447, 230294038, 342599089, 772808141, 4096882234, 3146662953,
2784264306, 1860954704, 2675279609, 2984212876, 2466966981, 2627986059, 2985545332,
2578042598, 1458940786, 2944243755, 3959506256, 1509151382, 325761900, 942251521,
4184289782, 2756231555, 3297811774, 1169708099, 3280524138, 3805245319, 3227360276,
3199632491, 2235795585, 2865407118, 36763651, 2441503575, 3314890374, 1755526087,
17915536, 1196948233, 949343045, 3815841867, 489007833, 2654997597, 2834744136,
417688687, 2843220846, 85621843, 747339336, 2043645709, 3520444394, 1825470818,
647778910, 275904777, 1249389189, 3640887431, 4200779599, 323384601, 3446088641,
4049835786, 1718989062, 3563787136, 44099190, 3281263107, 22910812, 1826109246,
745118154, 3392171319, 1571490704, 354891067, 815955642, 1453450421, 940015623,
796817754, 1260148619, 3898237757, 176670141, 1870249326, 3317738680, 448918002,
4059166594, 2003827551, 987091377, 224855998, 3520570137, 789522610, 2604445123,
454472869, 475688926, 2990723466, 523362238, 3897608102, 806637149, 2642229586,
2928614432, 1564415411, 1691381054, 3816907227, 4082581003, 1895544448, 3728217394,
3214813157, 4054301607, 1882632454, 2873728645, 3694943071, 1297991732, 2101682438,
3952579552, 678650400, 1391722293, 478833748, 2976468591, 158586606, 2576499787,
662690848, 3799889765, 3328894692, 2474578497, 2383901391, 1718193504, 3003184595,
3630561213, 1929441113, 3848238627, 1594310094, 3040359840, 3051803867, 2462788790,
954409915, 802581771, 681703307, 545982392, 2738993819, 8025358, 2827719383,
770471093, 3484895980, 3111306320, 3900000891, 2116916652, 397746721, 2087689510,
721433935, 1396088885, 2751612384, 1998988613, 2135074843, 2521131298, 707009172,
2398321482, 688041159, 2264560137, 482388305, 207864885, 3735036991, 3490348331,
1963642811, 3260224305, 3493564223, 1939428454, 1128799656, 1366012432, 2858822447,
1428147157, 2261125391, 1611208390, 1134826333, 2374102525, 3833625209, 2266397263,
3189115077, 770080230, 2674657172, 4280146640, 3604531615, 4235071805, 3436987249,
509704467, 2582695198, 4256268040, 3391197562, 1460642842, 1617931012, 457825497,
1031452907, 1330422862, 4125947620, 2280712485, 431892090, 2387410588, 2061126784,
896457479, 3480499461, 2488196663, 4021103792, 1877063114, 2744470201, 1046140599,
2129952955, 3583049218, 4217723693, 2720341743, 820661843, 1079873609, 3360954200,
3652304997, 3335838575, 2178810636, 1908053374, 4026721976, 1793145418, 476541615,
973420250, 515553040, 919292001, 2601786155, 1685119450, 3030170809, 1590676150,
1665099167, 651151584, 2077190587, 957892642, 646336572, 2743719258, 866169074,
851118829, 4225766285, 963748226, 799549420, 1955032629, 799460000, 2425744063,
2441291571, 1928963772, 528930629, 2591962884, 3495142819, 1896021824, 901320159,
3181820243, 843061941, 3338628510, 3782438992, 9515330, 1705797226, 953535929,
764833876, 3202464965, 2970244591, 519154982, 3390617541, 566616744, 3438031503,
1853838297, 170608755, 1393728434, 676900116, 3184965776, 1843100290, 78995357,
2227939888, 3460264600, 1745705055, 1474086965, 572796246, 4081303004, 882828851,
1295445825, 137639900, 3304579600, 2722437017, 4093422709, 273203373, 2666507854,
3998836510, 493829981, 1623949669, 3482036755, 3390023939, 833233937, 1639668730,
1499455075, 249728260, 1210694006, 3836497489, 1551488720, 3253074267, 3388238003,
2372035079, 3945715164, 2029501215, 3362012634, 2007375355, 4074709820, 631485888,
3135015769, 4273087084, 3648076204, 2739943601, 1374020358, 1760722448, 3773939706,
1313027823, 1895251226, 4224465911, 421382535, 1141067370, 3660034846, 3393185650,
1850995280, 1451917312, 3841455409, 3926840308, 1397397252, 2572864479, 2500171350,
3119920613, 531400869, 1626487579, 1099320497, 407414753, 2438623324, 99073255,
3175491512, 656431560, 1153671785, 236307875, 2824738046, 2320621382, 892174056,
230984053, 719791226, 2718891946, 624), None)
self.random = random.Random()
self.random.setstate(fixedState)
"""
Data structures useful for implementing SearchAgents
"""
class Stack:
"A container with a last-in-first-out (LIFO) queuing policy."
def __init__(self):
self.list = []
def push(self, item):
"Push 'item' onto the stack"
self.list.append(item)
def pop(self):
"Pop the most recently pushed item from the stack"
return self.list.pop()
def isEmpty(self):
"Returns true if the stack is empty"
return len(self.list) == 0
class Queue:
"A container with a first-in-first-out (FIFO) queuing policy."
def __init__(self):
self.list = []
def push(self, item):
"Enqueue the 'item' into the queue"
self.list.insert(0, item)
def pop(self):
"""
Dequeue the earliest enqueued item still in the queue. This
operation removes the item from the queue.
"""
return self.list.pop()
def isEmpty(self):
"Returns true if the queue is empty"
return len(self.list) == 0
class PriorityQueue:
"""
Implements a priority queue data structure. Each inserted item
has a priority associated with it and the client is usually interested
in quick retrieval of the lowest-priority item in the queue. This
data structure allows O(1) access to the lowest-priority item.
"""
def __init__(self):
self.heap = []
self.count = 0
def push(self, item, priority):
entry = (priority, self.count, item)
heapq.heappush(self.heap, entry)
self.count += 1
def pop(self):
(_, _, item) = heapq.heappop(self.heap)
return item
def isEmpty(self):
return len(self.heap) == 0
def update(self, item, priority):
# If item already in priority queue with higher priority, update its priority and rebuild the heap.
# If item already in priority queue with equal or lower priority, do nothing.
# If item not in priority queue, do the same thing as self.push.
for index, (p, c, i) in enumerate(self.heap):
if i == item:
if p <= priority:
break
del self.heap[index]
self.heap.append((priority, c, item))
heapq.heapify(self.heap)
break
else:
self.push(item, priority)
class PriorityQueueWithFunction(PriorityQueue):
"""
Implements a priority queue with the same push/pop signature of the
Queue and the Stack classes. This is designed for drop-in replacement for
those two classes. The caller has to provide a priority function, which
extracts each item's priority.
"""
def __init__(self, priorityFunction):
"priorityFunction (item) -> priority"
self.priorityFunction = priorityFunction # store the priority function
PriorityQueue.__init__(self) # super-class initializer
def push(self, item):
"Adds an item to the queue with priority from the priority function"
PriorityQueue.push(self, item, self.priorityFunction(item))
def manhattanDistance(xy1, xy2):
"Returns the Manhattan distance between points xy1 and xy2"
return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])
"""
Data structures and functions useful for various course projects
The search project should not need anything below this line.
"""
class Counter(dict):
"""
A counter keeps track of counts for a set of keys.
The counter class is an extension of the standard python
dictionary type. It is specialized to have number values
(integers or floats), and includes a handful of additional
functions to ease the task of counting data. In particular,
all keys are defaulted to have value 0. Using a dictionary:
a = {}
print a['test']
would give an error, while the Counter class analogue:
>>> a = Counter()
>>> print a['test']
0
returns the default 0 value. Note that to reference a key
that you know is contained in the counter,
you can still use the dictionary syntax:
>>> a = Counter()
>>> a['test'] = 2
>>> print a['test']
2
This is very useful for counting things without initializing their counts,
see for example:
>>> a['blah'] += 1
>>> print a['blah']
1
The counter also includes additional functionality useful in implementing
the classifiers for this assignment. Two counters can be added,
subtracted or multiplied together. See below for details. They can
also be normalized and their total count and arg max can be extracted.
"""
def __getitem__(self, idx):
self.setdefault(idx, 0)
return dict.__getitem__(self, idx)
def incrementAll(self, keys, count):
"""
Increments all elements of keys by the same count.
>>> a = Counter()
>>> a.incrementAll(['one','two', 'three'], 1)
>>> a['one']
1
>>> a['two']
1
"""
for key in keys:
self[key] += count
def argMax(self):
"""
Returns the key with the highest value.
"""
if len(list(self.keys())) == 0:
return None
all = list(self.items())
values = [x[1] for x in all]
maxIndex = values.index(max(values))
return all[maxIndex][0]
def sortedKeys(self):
"""
Returns a list of keys sorted by their values. Keys
with the highest values will appear first.
>>> a = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> a['third'] = 1
>>> a.sortedKeys()
['second', 'third', 'first']
"""
sortedItems = list(self.items())
def compare(x, y): return sign(y[1] - x[1])
sortedItems.sort(cmp=compare)
return [x[0] for x in sortedItems]
def totalCount(self):
"""
Returns the sum of counts for all keys.
"""
return sum(self.values())
def normalize(self):
"""
Edits the counter such that the total count of all
keys sums to 1. The ratio of counts for all keys
will remain the same. Note that normalizing an empty
Counter will result in an error.
"""
total = float(self.totalCount())
if total == 0:
return
for key in list(self.keys()):
self[key] = self[key] / total
def divideAll(self, divisor):
"""
Divides all counts by divisor
"""
divisor = float(divisor)
for key in self:
self[key] /= divisor
def copy(self):
"""
Returns a copy of the counter
"""
return Counter(dict.copy(self))
def __mul__(self, y):
"""
Multiplying two counters gives the dot product of their vectors where
each unique label is a vector element.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['second'] = 5
>>> a['third'] = 1.5
>>> a['fourth'] = 2.5
>>> a * b
14
"""
sum = 0
x = self
if len(x) > len(y):
x, y = y, x
for key in x:
if key not in y:
continue
sum += x[key] * y[key]
return sum
def __radd__(self, y):
"""
Adding another counter to a counter increments the current counter
by the values stored in the second counter.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['third'] = 1
>>> a += b
>>> a['first']
1
"""
for key, value in list(y.items()):
self[key] += value
def __add__(self, y):
"""
Adding two counters gives a counter with the union of all keys and
counts of the second added to counts of the first.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['third'] = 1
>>> (a + b)['first']
1
"""
addend = Counter()
for key in self:
if key in y:
addend[key] = self[key] + y[key]
else:
addend[key] = self[key]
for key in y:
if key in self:
continue
addend[key] = y[key]
return addend
def __sub__(self, y):
"""
Subtracting a counter from another gives a counter with the union of all keys and
counts of the second subtracted from counts of the first.
>>> a = Counter()
>>> b = Counter()
>>> a['first'] = -2
>>> a['second'] = 4
>>> b['first'] = 3
>>> b['third'] = 1
>>> (a - b)['first']
-5
"""
addend = Counter()
for key in self:
if key in y:
addend[key] = self[key] - y[key]
else:
addend[key] = self[key]
for key in y:
if key in self:
continue
addend[key] = -1 * y[key]
return addend
def raiseNotDefined():
fileName = inspect.stack()[1][1]
line = inspect.stack()[1][2]
method = inspect.stack()[1][3]
print("*** Method not implemented: %s at line %s of %s" %
(method, line, fileName))
sys.exit(1)
def normalize(vectorOrCounter):
"""
Normalize a vector or counter by dividing each value by the sum of all values
"""
normalizedCounter = Counter()
if type(vectorOrCounter) == type(normalizedCounter):
counter = vectorOrCounter
total = float(counter.totalCount())
if total == 0:
return counter
for key in list(counter.keys()):
value = counter[key]
normalizedCounter[key] = value / total
return normalizedCounter
else:
vector = vectorOrCounter
s = float(sum(vector))
if s == 0:
return vector
return [el / s for el in vector]
def nSample(distribution, values, n):
if sum(distribution) != 1:
distribution = normalize(distribution)
rand = [random.random() for i in range(n)]
rand.sort()
samples = []
samplePos, distPos, cdf = 0, 0, distribution[0]
while samplePos < n:
if rand[samplePos] < cdf:
samplePos += 1
samples.append(values[distPos])
else:
distPos += 1
cdf += distribution[distPos]
return samples
def sample(distribution, values=None):
if type(distribution) == Counter:
items = sorted(distribution.items())
distribution = [i[1] for i in items]
values = [i[0] for i in items]
if sum(distribution) != 1:
distribution = normalize(distribution)
choice = random.random()
i, total = 0, distribution[0]
while choice > total:
i += 1
total += distribution[i]
return values[i]
def sampleFromCounter(ctr):
items = sorted(ctr.items())
return sample([v for k, v in items], [k for k, v in items])
def getProbability(value, distribution, values):
"""
Gives the probability of a value under a discrete distribution
defined by (distributions, values).
"""
total = 0.0
for prob, val in zip(distribution, values):
if val == value:
total += prob
return total
def flipCoin(p):
r = random.random()
return r < p
def chooseFromDistribution(distribution):
"Takes either a counter or a list of (prob, key) pairs and samples"
if type(distribution) == dict or type(distribution) == Counter:
return sample(distribution)
r = random.random()
base = 0.0
for prob, element in distribution:
base += prob
if r <= base:
return element
def nearestPoint(pos):
"""
Finds the nearest grid point to a position (discretizes).
"""
(current_row, current_col) = pos
grid_row = int(current_row + 0.5)
grid_col = int(current_col + 0.5)
return (grid_row, grid_col)
def sign(x):
"""
Returns 1 or -1 depending on the sign of x
"""
if(x >= 0):
return 1
else:
return -1
def arrayInvert(array):
"""
Inverts a matrix stored as a list of lists.
"""
result = [[] for i in array]
for outer in array:
for inner in range(len(outer)):
result[inner].append(outer[inner])
return result
def matrixAsList(matrix, value=True):
"""
Turns a matrix into a list of coordinates matching the specified value
"""
rows, cols = len(matrix), len(matrix[0])
cells = []
for row in range(rows):
for col in range(cols):
if matrix[row][col] == value:
cells.append((row, col))
return cells
def lookup(name, namespace):
"""
Get a method or class from any imported module from its name.
Usage: lookup(functionName, globals())
"""
dots = name.count('.')
if dots > 0:
moduleName, objName = '.'.join(
name.split('.')[:-1]), name.split('.')[-1]
module = __import__(moduleName)
return getattr(module, objName)
else:
modules = [obj for obj in list(namespace.values()) if str(
type(obj)) == ""]
options = [getattr(module, name)
for module in modules if name in dir(module)]
options += [obj[1]
for obj in list(namespace.items()) if obj[0] == name]
if len(options) == 1:
return options[0]
if len(options) > 1:
raise Exception('Name conflict for %s')
raise Exception('%s not found as a method or class' % name)
def pause():
"""
Pauses the output stream awaiting user feedback.
"""
print("")
input()
# code to handle timeouts
#
# FIXME
# NOTE: TimeoutFuncton is NOT reentrant. Later timeouts will silently
# disable earlier timeouts. Could be solved by maintaining a global list
# of active time outs. Currently, questions which have test cases calling
# this have all student code so wrapped.
#
import signal
import time
class TimeoutFunctionException(Exception):
"""Exception to raise on a timeout"""
pass
class TimeoutFunction:
def __init__(self, function, timeout):
self.timeout = timeout
self.function = function
def handle_timeout(self, signum, frame):
raise TimeoutFunctionException()
def __call__(self, *args, **keyArgs):
# If we have SIGALRM signal, use it to cause an exception if and
# when this function runs too long. Otherwise check the time taken
# after the method has returned, and throw an exception then.
if hasattr(signal, 'SIGALRM'):
old = signal.signal(signal.SIGALRM, self.handle_timeout)
signal.alarm(self.timeout)
try:
result = self.function(*args, **keyArgs)
finally:
signal.signal(signal.SIGALRM, old)
signal.alarm(0)
else:
startTime = time.time()
result = self.function(*args, **keyArgs)
timeElapsed = time.time() - startTime
if timeElapsed >= self.timeout:
self.handle_timeout(None, None)
return result
_ORIGINAL_STDOUT = None
_ORIGINAL_STDERR = None
_MUTED = False
class WritableNull:
def write(self, string):
pass
def mutePrint():
global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED
if _MUTED:
return
_MUTED = True
_ORIGINAL_STDOUT = sys.stdout
#_ORIGINAL_STDERR = sys.stderr
sys.stdout = WritableNull()
#sys.stderr = WritableNull()
def unmutePrint():
global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED
if not _MUTED:
return
_MUTED = False
sys.stdout = _ORIGINAL_STDOUT
#sys.stderr = _ORIGINAL_STDERR
homework_2/VERSION
v1.004
Homework 2 (
1
0% of total course weight) – Multiagent Search
California State University San Bernardino, School of Computer Science and Engineering (CSE)
Date of Issue: February 24, 2020, Date of submission: March 10, 2021 – 11:59 pm (PST)
Module: CSE 5120 Introduction to Artificial Intelligence
Assessment brief: The code and resources provided in this homework are drawn from UC Berkeley which are part of their recent offering. Thanks, and credit to the authors (particularly Dan Klein, Pieter Abbeel, John DeNero, and others) at UC Berkeley for making these projects available to the public.
Pacman lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world efficiently will be Pacman’s first step in mastering his domain.
The code for this project consists of several Python files, some of which you will need to read and understand in order to complete the assignment, and some of which you can ignore. You can download all the code and supporting files as a
zip folder from homework 2 link given on Blackboard (multiagent.zip).
Your homework is based on two parts as given below:
1. Code implemented for multiagent algorithms in given multiAgents.py file (in specific sections as indicated in detail below)
2. A brief report on what you did for each algorithm (i.e., how you implemented with screenshots from autograder script given in the folder)
File Name
Description
multiAgents.py
Where all of your multi-agent search agents will reside.
pacman.py
The main file that runs Pacman games. This file describes a Pacman GameState type, which you use in this project.
game.py
The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid.
util.py
Useful data structures for implementing search algorithms.
After downloading the code, unzipping it, and changing to the directory, you should be able to play a game of Pacman by running the following command.
python pacman.py
pacman.py supports a number of options (e.g. --layout or -l). You can see the list of all options and their default values via python pacman.py -h.
You can use Spyder (installed through Anaconda from week 1 Thursday’s lecture) or other IDE for this work.
Files to Edit and Submit: You will need to edit and submit (multiAgents.py) file to implement your algorithms. Once you have completed the homework, you are welcome to run automated tests using an autograder.py given in the folder before you submit them for accuracy. You do not need to submit autograder.py file in your code submission but will need to test your algorithms with autograder.py to copy screenshots in your report. Please do not change the other files in this distribution or submit any of the original files other than these files.
Academic Dishonesty: Your code will be checked against other submissions in the class for logical redundancy. If you copy someone else’s code and submit it with minor changes, they will be detected easily, so please do not try that and submit your own work only. In case of cheating, the University’s academic policies on cheating and dishonesty will strictly apply which may result from the deduction in your grade to expulsion.
Getting Help: If you are having difficulty in implementing the algorithms from the pseudocodes provided in this homework, contact the course staff for help. Office hours and Slack are there for your support. If you are not able to attend office hours, then please inform your instructor to arrange for additional time. The intent is to make these projects rewarding and instructional, not frustrating and demoralizing. You can either complete this homework on your own or discuss the problem and collaborate with another member of the class (or different section). Please clearly acknowledge and mention your group member in your homework report submission who you will collaborate with in this homework. Your report and program (search.py file) will be separately submitted by yourself on Blackboard irrespective of your collaboration with your group member. Group discussions are encouraged but copying of programs is NOT recommended. Programming based on your own skills is encouraged.
Tasks for homework 2
1. Minimax search (3%)
Write an adversarial agent in the provided MinimaxAgent class in multiAgents.py. Your minimax agent should work with any number of ghosts, so your algorithm should be a more generalized version of the standard Minimax algorithm that we have studied in the class. Your minimax tree will have multiple min layers (one for each ghost) for each max layer. Your code should also be able to expand the tree to an arbitrary depth which can be accessed from self.depth and score your nodes with the supplied self.evaluationFunction. Make sure your Minimax program refers to these variables since these are populated in response to the command line options.
Important:
A single search ply is considered to be one Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman and each ghost moving two times. For further reading and understanding of Minimax (including alpha-beta pruning), please see
this short video tutorial
with pseudo code.
Hints and Observations
· Hint: Implement the algorithm recursively using helper function(s).
· The correct implementation of minimax will lead to Pacman losing the game in some tests. This is not a problem: as it is correct behavior, it will pass the tests.
· The evaluation function for the Pacman test in this part is implemented for you (self.evaluationFunction). You should not change this function but recognize that now we are evaluating states rather than actions, as compared to the reflex agent. Look-ahead agents evaluate future states whereas reflex agents evaluate actions from the current state.
· Pacman is always agent 0, and the agents move (take turns) in order of increasing agent index.
· All states in minimax should be GameStates, either passed in to getAction or generated via GameState.generateSuccessor. In this project, you will not be abstracting to simplified states.
Evaluation: Your code will be checked to determine whether it explores the correct number of game states. This is the only reliable way to detect some very subtle bugs in implementations of minimax. As a result, the autograder will be very picky about how many times you call GameState.generateSuccessor. If you call it any more or less than necessary, the autograder will complain. Please note that q1 relates to ReflexAgent which is not a part of this homework and can be skipped. We will start with q2 in this homework. To test and debug your code, run
python autograder.py -q q2
This will show what your algorithm does on a number of small trees, as well as a pacman game. To run it without graphics, use:
python autograder.py -q q2 --no-graphics
Figure 1: Pseudo-code for the implementation of Minimax algorithm. Please use this as a guide only. You will still need to carefully read multiAgents.py file for helper functions given in the comments and think/reason about the implementation of Minimax in Pacman scenario.
Figure 2: Pseudo-code for general-purpose implementation of Minimax algorithm. Please use this as a guide only. You will still need to carefully read multiAgents.py file for helper functions given in the comments and think/reason about the implementation of Minimax in Pacman scenario.
2. Alpha-beta pruning (2%)
write an adversarial agent in the provided AlphaBetaAgent class in multiAgents.py to more efficiently explore the minimax tree. Your agent should work with any number of ghosts, so your algorithm should be a generalized version of the standard Alpha-Beta Pruning algorithm. The AlphaBetaAgent minimax values should be identical to the MinimaxAgent minimax values, although the actions it selects can vary because of different tie-breaking behavior.
Note: The correct implementation of alpha-beta pruning will lead to Pacman losing some of the tests. This is not a problem: as it is correct behavior, it will pass the tests.
Evaluation: Your code will be checked to determine whether it explores the correct number of game states. Therefore, it is important that you perform alpha-beta pruning without reordering children. In other words, successor states should always be processed in the order returned by GameState.getLegalActions. Again, do not call GameState.generateSuccessor more than necessary. Additionally, in order to match the set of states explored by the autograder, you must not prune on equality: that is, stop generating children for a max (min) node only if a child's value is strictly greater than (less than) β (α). To test and debug your code, run
python autograder.py -q q3
This will show what your algorithm does on a number of small trees, as well as a pacman game. To run it without graphics, use:
python autograder.py -q q3 --no-graphics
Figure 3: Pseudo-code for the implementation of the algorithm you should implement for this question. Please use this as a guide only. You will still need to carefully read multiAgents.py file for helper functions given in the comments and think/reason about the implementation of Minimax in Pacman scenario.
3. Expectimax Search (2%)
Implement the ExpectimaxAgent, which is useful for modeling probabilistic behavior of agents who may make suboptimal choices. As with the search and constraint satisfaction problems covered in CSE 5120, the impressive feature of this algorithm is its general applicability.
Note: The correct implementation of expectimax will lead to Pacman losing some of the tests. This is not a problem: as it is correct behavior, it will pass the tests.
Evaluation: You can debug your implementation on small the game trees using the command:
python autograder.py -q q4
Debugging on these small and manageable test cases is recommended and will help you to find bugs quickly. Once your algorithm is working on small trees, you can observe its success in Pacman. Random ghosts are not optimal minimax agents, and so modeling them with minimax search may not be appropriate. ExpectimaxAgent, does not take the min over all ghost actions, but the expectation according to the agent's model of how the ghosts act. To simplify your code, assume you will only be running against an adversary which chooses amongst their getLegalActions uniformly at random (read about uniform distribution for further understanding). To see how the ExpectimaxAgent behaves in Pacman, run:
python pacman.py -p ExpectimaxAgent -l minimaxClassic -a depth=3
You should now observe a more cavalier approach in close quarters with ghosts. In particular, if Pacman perceives that he could be trapped but might escape to grab a few more pieces of food, he will at least try. Investigate the results of these two scenarios:
python pacman.py -p AlphaBetaAgent -l trappedClassic -a depth=3 -q -n 10
python pacman.py -p ExpectimaxAgent -l trappedClassic -a depth=3 -q -n 10
You should find that your ExpectimaxAgent wins about half the time, while your AlphaBetaAgent always loses. Make sure you understand why the behavior here differs from the minimax case.
4. Constraint satisfaction problems (3%)
1. (1.5%) Consider the problem of placing k knights on an n×n chess board such that no two knights are attacking each other, where k is given and k ≤ n2.
· Choose a CSP formulation. What are the variables in your formulation?
· What are the possible values of each variable in your formulation?
· What sets of variables are constrained, and how?
2. (1.5%) Class scheduling (items to answer are at the end in green font)
You are given a problem of class scheduling for the computer science department at CSUSB. The class timings are Tuesdays, Thursdays, and Fridays. There are 5 different classes on these given days and 3 professors who are qualified for teaching these classes.
Problem constraint: Each professor can teach only one class at a time.
Classes:
C1 – CSE 5120 Introduction to Artificial Intelligence: Time: 1:00 - 2:15pm
C2 – CSE 4600 Operating Systems: Time: 9:00 – 10:15am
C3 – CSE 4550 Software Engineering: Time: 10:30-11:45am
C4 – CSE 5720 Database Systems: Time: 10:30 - 11:45am
C5 – CSE 5160 Machine Learning: Time: 2:30 - 3:45pm
Professors:
Professor A: Qualified to teach Classes 1, 2, and 5.
Professor B: Qualified to teach Classes 3, 4, and 5.
Professor C: Qualified to teach Classes 1, 3, and 4.
1. Formulate this problem as a CSP where there is one variable per class, reporting the domains and constraints (e.g., for each class, the entry in the table should be ). Also, list binary constraints on the classes . Your constraints should be specified formally which can be implicit rather than explicit
2. Draw the constraint graph for your problem in item 1
3. Make sure your CSP looks nearly tree-structure. Provide a one paragraph description of why the solution to CSP via tree structured CSPs is preferred. Review lecture slides and videos for help.
1
Homework 2 (10%)
CSE 5120 (Section ##) – Introduction to Artificial Intelligence – Spring 2021
Submitted to
Department of Computer Science and Engineering
California State University, San Bernardino, California
by
Student name (CSUSB ID)
(Your collaborator in this homework (if any))
Date: Month Day, Year
Email:
· Your email
· Your collaborator’s email (if you collaborated with any)
Report
Brief description of your work here acknowledging your collaboration with your class fellow (or a friend from other CSE 5120 section), and the capacity at which he/she collaborated with you, followed by the algorithms you implemented.
1. Minimax algorithm
Your brief explanation of the problem, your code solution, and any documentation with screenshots of your code Evaluation (results from autograder.py)
2. Alpha-beta pruning
Your brief explanation of the problem, your code solution, and any documentation with screenshots of your code Evaluation (results from autograder.py)
3. Expectimax Search
Your brief explanation of the problem, your code solution, and any documentation with screenshots of your code Evaluation (results from autograder.py)
4. Constraint satisfaction problems
Your explanation and drawings, wherever necessary, numbered according to how the questions are defined in the questions.
α-βImplementationdef min-value(state, α, β):initialize v = +∞for each successor of state:v = min(v, value(successor, α, β))if v ≤ αreturn vβ = min(β, v)return vdef max-value(state, α, β):initialize v = -∞for each successor of state:v = max(v, value(successor, α, β))ifv ≥ βreturn vα= max(α, v)return vα: MAX’s best option on path to rootβ:MIN’s best option on path to root
Minimax Implementationdef value(state):if the state is a terminal state: return the state’s utilityif the next agent is MAX: return max-value(state)if the next agent is MIN: return min-value(state)def min-value(state):initialize v = +∞for each successor of state:v = min(v, value(successor))return vdef max-value(state):initialize v = -∞for each successor of state:v = max(v, value(successor))return v
