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search/.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

search/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

search/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 2.7" project-jdk-type="Python SDK" />
+</project>

search/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/search.iml" filepath="$PROJECT_DIR$/.idea/search.iml" />
+    </modules>
+  </component>
+</project>

search/.idea/search.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 2.7" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

search/.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/search/Pacman" vcs="Git" />
+  </component>
+</project>

search/VERSION

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+v1.001

search/autograder.py

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+# 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).
+
+
+# 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] in 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': {
+    "<type 'exceptions.IndexError'>": """
+      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': {
+      "<type 'exceptions.AttributeError'>": """
+        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 = filter(lambda t: re.match('[^#~.].*\.test\Z', t), os.listdir(subdir_path))
+        tests = map(lambda t: re.match('(.*)\.test\Z', t).group(1), 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))

search/commands.txt

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+python pacman.py
+python pacman.py --layout testMaze --pacman GoWestAgent
+python pacman.py --layout tinyMaze --pacman GoWestAgent
+python pacman.py -h
+python pacman.py -l tinyMaze -p SearchAgent -a fn=tinyMazeSearch
+python pacman.py -l tinyMaze -p SearchAgent
+python pacman.py -l mediumMaze -p SearchAgent
+python pacman.py -l bigMaze -z .5 -p SearchAgent
+python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs
+python pacman.py -l bigMaze -p SearchAgent -a fn=bfs -z .5
+python eightpuzzle.py
+python pacman.py -l mediumMaze -p SearchAgent -a fn=ucs
+python pacman.py -l mediumDottedMaze -p StayEastSearchAgent
+python pacman.py -l mediumScaryMaze -p StayWestSearchAgent
+python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic 
+python pacman.py -l tinyCorners -p SearchAgent -a fn=bfs,prob=CornersProblem
+python pacman.py -l mediumCorners -p SearchAgent -a fn=bfs,prob=CornersProblem
+python pacman.py -l mediumCorners -p AStarCornersAgent -z 0.5
+python pacman.py -l testSearch -p AStarFoodSearchAgent
+python pacman.py -l trickySearch -p AStarFoodSearchAgent
+python pacman.py -l bigSearch -p ClosestDotSearchAgent -z .5 
+python pacman.py -l bigSearch -p ApproximateSearchAgent -z .5 -q 

search/eightpuzzle.py

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+# eightpuzzle.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 search
+import random
+
+# Module Classes
+
+class EightPuzzleState:
+    """
+    The Eight Puzzle is described in the course textbook on
+    page 64.
+
+    This class defines the mechanics of the puzzle itself.  The
+    task of recasting this puzzle as a search problem is left to
+    the EightPuzzleSearchProblem class.
+    """
+
+    def __init__( self, numbers ):
+        """
+          Constructs a new eight puzzle from an ordering of numbers.
+
+        numbers: a list of integers from 0 to 8 representing an
+          instance of the eight puzzle.  0 represents the blank
+          space.  Thus, the list
+
+            [1, 0, 2, 3, 4, 5, 6, 7, 8]
+
+          represents the eight puzzle:
+            -------------
+            | 1 |   | 2 |
+            -------------
+            | 3 | 4 | 5 |
+            -------------
+            | 6 | 7 | 8 |
+            ------------
+
+        The configuration of the puzzle is stored in a 2-dimensional
+        list (a list of lists) 'cells'.
+        """
+        self.cells = []
+        numbers = numbers[:] # Make a copy so as not to cause side-effects.
+        numbers.reverse()
+        for row in range( 3 ):
+            self.cells.append( [] )
+            for col in range( 3 ):
+                self.cells[row].append( numbers.pop() )
+                if self.cells[row][col] == 0:
+                    self.blankLocation = row, col
+
+    def isGoal( self ):
+        """
+          Checks to see if the puzzle is in its goal state.
+
+            -------------
+            |   | 1 | 2 |
+            -------------
+            | 3 | 4 | 5 |
+            -------------
+            | 6 | 7 | 8 |
+            -------------
+
+        >>> EightPuzzleState([0, 1, 2, 3, 4, 5, 6, 7, 8]).isGoal()
+        True
+
+        >>> EightPuzzleState([1, 0, 2, 3, 4, 5, 6, 7, 8]).isGoal()
+        False
+        """
+        current = 0
+        for row in range( 3 ):
+            for col in range( 3 ):
+                if current != self.cells[row][col]:
+                    return False
+                current += 1
+        return True
+
+    def legalMoves( self ):
+        """
+          Returns a list of legal moves from the current state.
+
+        Moves consist of moving the blank space up, down, left or right.
+        These are encoded as 'up', 'down', 'left' and 'right' respectively.
+
+        >>> EightPuzzleState([0, 1, 2, 3, 4, 5, 6, 7, 8]).legalMoves()
+        ['down', 'right']
+        """
+        moves = []
+        row, col = self.blankLocation
+        if(row != 0):
+            moves.append('up')
+        if(row != 2):
+            moves.append('down')
+        if(col != 0):
+            moves.append('left')
+        if(col != 2):
+            moves.append('right')
+        return moves
+
+    def result(self, move):
+        """
+          Returns a new eightPuzzle with the current state and blankLocation
+        updated based on the provided move.
+
+        The move should be a string drawn from a list returned by legalMoves.
+        Illegal moves will raise an exception, which may be an array bounds
+        exception.
+
+        NOTE: This function *does not* change the current object.  Instead,
+        it returns a new object.
+        """
+        row, col = self.blankLocation
+        if(move == 'up'):
+            newrow = row - 1
+            newcol = col
+        elif(move == 'down'):
+            newrow = row + 1
+            newcol = col
+        elif(move == 'left'):
+            newrow = row
+            newcol = col - 1
+        elif(move == 'right'):
+            newrow = row
+            newcol = col + 1
+        else:
+            raise "Illegal Move"
+
+        # Create a copy of the current eightPuzzle
+        newPuzzle = EightPuzzleState([0, 0, 0, 0, 0, 0, 0, 0, 0])
+        newPuzzle.cells = [values[:] for values in self.cells]
+        # And update it to reflect the move
+        newPuzzle.cells[row][col] = self.cells[newrow][newcol]
+        newPuzzle.cells[newrow][newcol] = self.cells[row][col]
+        newPuzzle.blankLocation = newrow, newcol
+
+        return newPuzzle
+
+    # Utilities for comparison and display
+    def __eq__(self, other):
+        """
+            Overloads '==' such that two eightPuzzles with the same configuration
+          are equal.
+
+          >>> EightPuzzleState([0, 1, 2, 3, 4, 5, 6, 7, 8]) == \
+              EightPuzzleState([1, 0, 2, 3, 4, 5, 6, 7, 8]).result('left')
+          True
+        """
+        for row in range( 3 ):
+            if self.cells[row] != other.cells[row]:
+                return False
+        return True
+
+    def __hash__(self):
+        return hash(str(self.cells))
+
+    def __getAsciiString(self):
+        """
+          Returns a display string for the maze
+        """
+        lines = []
+        horizontalLine = ('-' * (13))
+        lines.append(horizontalLine)
+        for row in self.cells:
+            rowLine = '|'
+            for col in row:
+                if col == 0:
+                    col = ' '
+                rowLine = rowLine + ' ' + col.__str__() + ' |'
+            lines.append(rowLine)
+            lines.append(horizontalLine)
+        return '\n'.join(lines)
+
+    def __str__(self):
+        return self.__getAsciiString()
+
+# TODO: Implement The methods in this class
+
+class EightPuzzleSearchProblem(search.SearchProblem):
+    """
+      Implementation of a SearchProblem for the  Eight Puzzle domain
+
+      Each state is represented by an instance of an eightPuzzle.
+    """
+    def __init__(self,puzzle):
+        "Creates a new EightPuzzleSearchProblem which stores search information."
+        self.puzzle = puzzle
+
+    def getStartState(self):
+        return puzzle
+
+    def isGoalState(self,state):
+        return state.isGoal()
+
+    def getSuccessors(self,state):
+        """
+          Returns list of (successor, action, stepCost) pairs where
+          each succesor is either left, right, up, or down
+          from the original state and the cost is 1.0 for each
+        """
+        succ = []
+        for a in state.legalMoves():
+            succ.append((state.result(a), a, 1))
+        return succ
+
+    def getCostOfActions(self, actions):
+        """
+         actions: A list of actions to take
+
+        This method returns the total cost of a particular sequence of actions.  The sequence must
+        be composed of legal moves
+        """
+        return len(actions)
+
+EIGHT_PUZZLE_DATA = [[1, 0, 2, 3, 4, 5, 6, 7, 8],
+                     [1, 7, 8, 2, 3, 4, 5, 6, 0],
+                     [4, 3, 2, 7, 0, 5, 1, 6, 8],
+                     [5, 1, 3, 4, 0, 2, 6, 7, 8],
+                     [1, 2, 5, 7, 6, 8, 0, 4, 3],
+                     [0, 3, 1, 6, 8, 2, 7, 5, 4]]
+
+def loadEightPuzzle(puzzleNumber):
+    """
+      puzzleNumber: The number of the eight puzzle to load.
+
+      Returns an eight puzzle object generated from one of the
+      provided puzzles in EIGHT_PUZZLE_DATA.
+
+      puzzleNumber can range from 0 to 5.
+
+      >>> print loadEightPuzzle(0)
+      -------------
+      | 1 |   | 2 |
+      -------------
+      | 3 | 4 | 5 |
+      -------------
+      | 6 | 7 | 8 |
+      -------------
+    """
+    return EightPuzzleState(EIGHT_PUZZLE_DATA[puzzleNumber])
+
+def createRandomEightPuzzle(moves=100):
+    """
+      moves: number of random moves to apply
+
+      Creates a random eight puzzle by applying
+      a series of 'moves' random moves to a solved
+      puzzle.
+    """
+    puzzle = EightPuzzleState([0,1,2,3,4,5,6,7,8])
+    for i in range(moves):
+        # Execute a random legal move
+        puzzle = puzzle.result(random.sample(puzzle.legalMoves(), 1)[0])
+    return puzzle
+
+if __name__ == '__main__':
+    puzzle = createRandomEightPuzzle(25)
+    print('A random puzzle:')
+    print(puzzle)
+
+    problem = EightPuzzleSearchProblem(puzzle)
+    path = search.breadthFirstSearch(problem)
+    print('BFS found a path of %d moves: %s' % (len(path), str(path)))
+    curr = puzzle
+    i = 1
+    for a in path:
+        curr = curr.result(a)
+        print('After %d move%s: %s' % (i, ("", "s")[i>1], a))
+        print(curr)
+
+        raw_input("Press return for the next state...")   # wait for key stroke
+        i += 1

search/game.py

@@ -0,0 +1,729 @@
+# 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, 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 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
+        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.NORTH: (0, 1),
+                   Directions.SOUTH: (0, -1),
+                   Directions.EAST:  (1, 0),
+                   Directions.WEST:  (-1, 0),
+                   Directions.STOP:  (0, 0)}
+
+    _directionsAsList = _directions.items()
+
+    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, 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 cStringIO
+        self.agentOutput = [cStringIO.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 cStringIO
+        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 >>sys.stderr, "Agent %d failed to load" % i
+                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 >>sys.stderr, "Agent %d ran out of time on startup!" % i
+                            self.unmute()
+                            self.agentTimeout = True
+                            self._agentCrash(i, quiet=True)
+                            return
+                    except Exception,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,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 >>sys.stderr, "Agent %d timed out on a single move!" % agentIndex
+                        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 >>sys.stderr, "Agent %d took too long to make a move! This is warning %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex])
+                        if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
+                            print >>sys.stderr, "Agent %d exceeded the maximum number of warnings: %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex])
+                            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 >>sys.stderr, "Agent %d ran out of time! (time: %1.2f)" % (agentIndex, self.totalAgentTimes[agentIndex])
+                        self.agentTimeout = True
+                        self._agentCrash(agentIndex, quiet=True)
+                        self.unmute()
+                        return
+                    self.unmute()
+                except Exception,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,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,data:
+                    if not self.catchExceptions: raise
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+        self.display.finish()

search/ghostAgents.py

@@ -0,0 +1,81 @@
+# 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

search/grading.py

@@ -0,0 +1,323 @@
+# 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, 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 <pre> 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("<div>")
+
+    # first sum
+    total_possible = sum(self.maxes.values())
+    total_score = sum(self.points.values())
+    checkOrX = '<span class="incorrect"/>'
+    if (total_score >= total_possible):
+        checkOrX = '<span class="correct"/>'
+    header = """
+        <h3>
+            Total score ({total_score} / {total_possible})
+        </h3>
+    """.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 = '<span class="incorrect"/>'
+      if (self.points[q] >= self.maxes[q]):
+        checkOrX = '<span class="correct"/>'
+      #messages = '\n<br/>\n'.join(self.messages[q])
+      messages = "<pre>%s</pre>" % '\n'.join(self.messages[q])
+      output = """
+        <div class="test">
+          <section>
+          <div class="shortform">
+            Question {q} ({points}/{max}) {checkOrX}
+          </div>
+        <div class="longform">
+          {messages}
+        </div>
+        </section>
+      </div>
+      """.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("</div>")
+    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())
+

search/graphicsDisplay.py

@@ -0,0 +1,679 @@
+# 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, 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 = 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 = map(lambda x: x.copy(), 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

search/graphicsUtils.py

@@ -0,0 +1,402 @@
+# 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 map(lambda x: int(x, 16) / 256.0, [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>", _keypress )
+    _root_window.bind( "<KeyRelease>", _keyrelease )
+    _root_window.bind( "<FocusIn>", _clear_keys )
+    _root_window.bind( "<FocusOut>", _clear_keys )
+    _root_window.bind( "<Button-1>", _leftclick )
+    _root_window.bind( "<Button-2>", _rightclick )
+    _root_window.bind( "<Button-3>", _rightclick )
+    _root_window.bind( "<Control-Button-1>", _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, 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=Tkinter.tkinter.dooneevent,
+                 d_w=Tkinter.tkinter.DONT_WAIT):
+    d_o_e(d_w)
+    if _got_release:
+        d_o_e(d_w)
+    return _keysdown.keys()
+
+def keys_waiting():
+    global _keyswaiting
+    keys = _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=Tkinter.tkinter.dooneevent,
+                       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=Tkinter.tkinter.dooneevent,
+            d_w=Tkinter.tkinter.DONT_WAIT):
+    if y is None:
+        try: x, y = x
+        except: raise  '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=Tkinter.tkinter.dooneevent,
+            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)

search/keyboardAgents.py

@@ -0,0 +1,84 @@
+# 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

search/layout.py

@@ -0,0 +1,149 @@
+# 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
+
+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(range(self.width))
+        y = random.choice(range(self.height))
+        while self.isWall( (x, y) ):
+            x = random.choice(range(self.width))
+            y = random.choice(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()

search/layouts/bigCorners.lay

@@ -0,0 +1,37 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%.      %                         %.%
+%         %%%%% % %%% %%% %%%%%%% % %
+%       %       % %     %     % %   %
+%%%%% %%%%% %%% % % % %%% %%%%% % %%%
+%   % % % %   % % % %   % %     %   %
+% %%% % % % %%% %%%%% %%% % %%% %%% %
+%       %     %   %   %     % % %   %
+%%% %%%%%%%%% %%%%%%% %%% %%% % % % %
+%             %       % %   %     % %
+% % %%%%% % %%% % % %%% % %%% %%% % %
+% % %     % % % % %     %   % % % % %
+% % % %%%%%%% % %%%%%%%%% %%% % %%% %
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search/layouts/trickyClassic.lay

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search/pacman.py

@@ -0,0 +1,684 @@
+# 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, layout
+import sys, types, time, random, 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 <options>
+    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 cPickle
+        f = open(options.gameToReplay)
+        try: recorded = cPickle.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, 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, cPickle
+            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}
+            cPickle.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

search/pacmanAgents.py

@@ -0,0 +1,52 @@
+# 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()

search/projectParams.py

@@ -0,0 +1,18 @@
+# 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 = 'searchAgents.py,search.py'
+PROJECT_TEST_CLASSES = 'searchTestClasses.py'
+PROJECT_NAME = 'Project 1: Search'
+BONUS_PIC = False

search/search.py

@@ -0,0 +1,191 @@
+# search.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).
+
+
+"""
+In search.py, you will implement generic search algorithms which are called by
+Pacman agents (in searchAgents.py).
+"""
+
+import util
+
+class SearchProblem:
+    """
+    This class outlines the structure of a search problem, but doesn't implement
+    any of the methods (in object-oriented terminology: an abstract class).
+
+    You do not need to change anything in this class, ever.
+    """
+
+    def getStartState(self):
+        """
+        Returns the start state for the search problem.
+        """
+        util.raiseNotDefined()
+
+    def isGoalState(self, state):
+        """
+          state: Search state
+
+        Returns True if and only if the state is a valid goal state.
+        """
+        util.raiseNotDefined()
+
+    def getSuccessors(self, state):
+        """
+          state: Search state
+
+        For a given state, this should return a list of triples, (successor,
+        action, stepCost), where 'successor' is a successor to the current
+        state, 'action' is the action required to get there, and 'stepCost' is
+        the incremental cost of expanding to that successor.
+        """
+        util.raiseNotDefined()
+
+    def getCostOfActions(self, actions):
+        """
+         actions: A list of actions to take
+
+        This method returns the total cost of a particular sequence of actions.
+        The sequence must be composed of legal moves.
+        """
+        util.raiseNotDefined()
+
+
+def tinyMazeSearch(problem):
+    """
+    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
+    sequence of moves will be incorrect, so only use this for tinyMaze.
+    """
+    from game import Directions
+    s = Directions.SOUTH
+    w = Directions.WEST
+    return  [s, s, w, s, w, w, s, w]
+
+def depthFirstSearch(problem):
+    """
+    Search the deepest nodes in the search tree first.
+
+    Your search algorithm needs to return a list of actions that reaches the
+    goal. Make sure to implement a graph search algorithm.
+
+    To get started, you might want to try some of these simple commands to
+    understand the search problem that is being passed in:
+
+    print "Start:", problem.getStartState()
+    print "Is the start a goal?", problem.isGoalState(problem.getStartState())
+    print "Start's successors:", problem.getSuccessors(problem.getStartState())
+    """
+    "*** YOUR CODE HERE ***"
+# start state
+    startState = problem.getStartState()
+# have exit
+    exitState = []
+#use stack to dfs
+    States = util.Stack()
+    States.push((startState,[]))
+#repeat a circle to confirm if it is successful
+    while (not States.isEmpty()) and (not problem.isGoalState(startState)):
+        State,Actions = States.pop()
+        if State not in exitState:
+            exitState.append(State)
+            Successor = problem.getSuccessors(State)
+            for Node in Successor:
+                Coordinates = Node[0]
+                Direction = Node[1]
+                if not Coordinates in exitState:
+                    States.push((Coordinates,Actions + [Direction]))
+                startState = Coordinates
+    return Actions + [Direction]
+    util.raiseNotDefined()
+
+def breadthFirstSearch(problem):
+    """Search the shallowest nodes in the search tree first."""
+    "*** YOUR CODE HERE ***"
+    startState = problem.getStartState()
+    exitState = []
+    States = util.Queue()
+    States.push((startState,[]))
+    while (not States.isEmpty()) and (not problem.isGoalState(startState)):
+        State, Actions = States.pop()
+        if State not in exitState:
+            Successor = problem.getSuccessors(State)
+            exitState.append(State)
+            for Node in Successor:
+                Coordinates = Node[0]
+                Direction = Node[1]
+                if Coordinates not in exitState:
+                    States.push((Coordinates,Actions+[Direction]))
+                startState = Coordinates
+    return Actions + [Direction]
+    util.raiseNotDefined()
+
+def uniformCostSearch(problem):
+    """Search the node of least total cost first."""
+    "*** YOUR CODE HERE ***"
+    startState = problem.getStartState()
+    exitState = []
+    States = util.PriorityQueue()
+    States.push((startState, []),0)
+    while (not States.isEmpty()) and (not problem.isGoalState(startState)):
+        State, Actions = States.pop()
+        if State not in exitState:
+            Successor = problem.getSuccessors(State)
+            exitState.append(State)
+            for Node in Successor:
+                Coordinates = Node[0]
+                Direction = Node[1]
+                if Coordinates not in exitState:
+                    newAction = Actions + [Direction]
+                    States.push((Coordinates, newAction),problem.getCostOfActions(newAction))
+                startState = Coordinates
+    return Actions + [Direction]
+    util.raiseNotDefined()
+
+def nullHeuristic(state, problem=None):
+    """
+    A heuristic function estimates the cost from the current state to the nearest
+    goal in the provided SearchProblem.  This heuristic is trivial.
+    """
+    return 0
+
+def aStarSearch(problem, heuristic=nullHeuristic):
+    """Search the node that has the lowest combined cost and heuristic first."""
+    "*** YOUR CODE HERE ***"
+    startState = problem.getStartState()
+    exitState = []
+    States = util.PriorityQueue()
+    h_n = heuristic(startState,problem)
+    States.push((startState, []), h_n)
+    while (not States.isEmpty()) and (not problem.isGoalState(startState)):
+        State, Actions = States.pop()
+        if State not in exitState:
+            Successor = problem.getSuccessors(State)
+            exitState.append(State)
+            for Node in Successor:
+                Coordinates = Node[0]
+                Direction = Node[1]
+                if Coordinates not in exitState:
+                    newAction = Actions + [Direction]
+                    newCost = problem.getCostOfActions(newAction) + heuristic(Coordinates,problem)
+                    States.push((Coordinates, newAction), newCost)
+                startState = Coordinates
+    return Actions + [Direction]
+    util.raiseNotDefined()
+
+
+# Abbreviations
+bfs = breadthFirstSearch
+dfs = depthFirstSearch
+astar = aStarSearch
+ucs = uniformCostSearch

search/searchAgents.py

@@ -0,0 +1,654 @@
+# searchAgents.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).
+
+
+"""
+This file contains all of the agents that can be selected to control Pacman.  To
+select an agent, use the '-p' option when running pacman.py.  Arguments can be
+passed to your agent using '-a'.  For example, to load a SearchAgent that uses
+depth first search (dfs), run the following command:
+
+> python pacman.py -p SearchAgent -a fn=depthFirstSearch
+
+Commands to invoke other search strategies can be found in the project
+description.
+
+Please only change the parts of the file you are asked to.  Look for the lines
+that say
+
+"*** YOUR CODE HERE ***"
+
+The parts you fill in start about 3/4 of the way down.  Follow the project
+description for details.
+
+Good luck and happy searching!
+"""
+
+from game import Directions
+from game import Agent
+from game import Actions
+import util
+import time
+import search
+
+class GoWestAgent(Agent):
+    "An agent that goes West until it can't."
+
+    def getAction(self, state):
+        "The agent receives a GameState (defined in pacman.py)."
+        if Directions.WEST in state.getLegalPacmanActions():
+            return Directions.WEST
+        else:
+            return Directions.STOP
+
+#######################################################
+# This portion is written for you, but will only work #
+#       after you fill in parts of search.py          #
+#######################################################
+
+class SearchAgent(Agent):
+    """
+    This very general search agent finds a path using a supplied search
+    algorithm for a supplied search problem, then returns actions to follow that
+    path.
+
+    As a default, this agent runs DFS on a PositionSearchProblem to find
+    location (1,1)
+
+    Options for fn include:
+      depthFirstSearch or dfs
+      breadthFirstSearch or bfs
+
+
+    Note: You should NOT change any code in SearchAgent
+    """
+
+    def __init__(self, fn='depthFirstSearch', prob='PositionSearchProblem', heuristic='nullHeuristic'):
+        # Warning: some advanced Python magic is employed below to find the right functions and problems
+
+        # Get the search function from the name and heuristic
+        if fn not in dir(search):
+            raise AttributeError, fn + ' is not a search function in search.py.'
+        func = getattr(search, fn)
+        if 'heuristic' not in func.func_code.co_varnames:
+            print('[SearchAgent] using function ' + fn)
+            self.searchFunction = func
+        else:
+            if heuristic in globals().keys():
+                heur = globals()[heuristic]
+            elif heuristic in dir(search):
+                heur = getattr(search, heuristic)
+            else:
+                raise AttributeError, heuristic + ' is not a function in searchAgents.py or search.py.'
+            print('[SearchAgent] using function %s and heuristic %s' % (fn, heuristic))
+            # Note: this bit of Python trickery combines the search algorithm and the heuristic
+            self.searchFunction = lambda x: func(x, heuristic=heur)
+
+        # Get the search problem type from the name
+        if prob not in globals().keys() or not prob.endswith('Problem'):
+            raise AttributeError, prob + ' is not a search problem type in SearchAgents.py.'
+        self.searchType = globals()[prob]
+        print('[SearchAgent] using problem type ' + prob)
+
+    def registerInitialState(self, state):
+        """
+        This is the first time that the agent sees the layout of the game
+        board. Here, we choose a path to the goal. In this phase, the agent
+        should compute the path to the goal and store it in a local variable.
+        All of the work is done in this method!
+
+        state: a GameState object (pacman.py)
+        """
+        if self.searchFunction == None: raise Exception, "No search function provided for SearchAgent"
+        starttime = time.time()
+        problem = self.searchType(state) # Makes a new search problem
+        self.actions  = self.searchFunction(problem) # Find a path
+        totalCost = problem.getCostOfActions(self.actions)
+        print('Path found with total cost of %d in %.1f seconds' % (totalCost, time.time() - starttime))
+        if '_expanded' in dir(problem): print('Search nodes expanded: %d' % problem._expanded)
+
+    def getAction(self, state):
+        """
+        Returns the next action in the path chosen earlier (in
+        registerInitialState).  Return Directions.STOP if there is no further
+        action to take.
+
+        state: a GameState object (pacman.py)
+        """
+        if 'actionIndex' not in dir(self): self.actionIndex = 0
+        i = self.actionIndex
+        self.actionIndex += 1
+        if i < len(self.actions):
+            return self.actions[i]
+        else:
+            return Directions.STOP
+
+class PositionSearchProblem(search.SearchProblem):
+    """
+    A search problem defines the state space, start state, goal test, successor
+    function and cost function.  This search problem can be used to find paths
+    to a particular point on the pacman board.
+
+    The state space consists of (x,y) positions in a pacman game.
+
+    Note: this search problem is fully specified; you should NOT change it.
+    """
+
+    def __init__(self, gameState, costFn = lambda x: 1, goal=(1,1), start=None, warn=True, visualize=True):
+        """
+        Stores the start and goal.
+
+        gameState: A GameState object (pacman.py)
+        costFn: A function from a search state (tuple) to a non-negative number
+        goal: A position in the gameState
+        """
+        self.walls = gameState.getWalls()
+        self.startState = gameState.getPacmanPosition()
+        if start != None: self.startState = start
+        self.goal = goal
+        self.costFn = costFn
+        self.visualize = visualize
+        if warn and (gameState.getNumFood() != 1 or not gameState.hasFood(*goal)):
+            print 'Warning: this does not look like a regular search maze'
+
+        # For display purposes
+        self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE
+
+    def getStartState(self):
+        return self.startState
+
+    def isGoalState(self, state):
+        isGoal = state == self.goal
+
+        # For display purposes only
+        if isGoal and self.visualize:
+            self._visitedlist.append(state)
+            import __main__
+            if '_display' in dir(__main__):
+                if 'drawExpandedCells' in dir(__main__._display): #@UndefinedVariable
+                    __main__._display.drawExpandedCells(self._visitedlist) #@UndefinedVariable
+
+        return isGoal
+
+    def getSuccessors(self, state):
+        """
+        Returns successor states, the actions they require, and a cost of 1.
+
+         As noted in search.py:
+             For a given state, this should return a list of triples,
+         (successor, action, stepCost), where 'successor' is a
+         successor to the current state, 'action' is the action
+         required to get there, and 'stepCost' is the incremental
+         cost of expanding to that successor
+        """
+
+        successors = []
+        for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:
+            x,y = state
+            dx, dy = Actions.directionToVector(action)
+            nextx, nexty = int(x + dx), int(y + dy)
+            if not self.walls[nextx][nexty]:
+                nextState = (nextx, nexty)
+                cost = self.costFn(nextState)
+                successors.append( ( nextState, action, cost) )
+
+        # Bookkeeping for display purposes
+        self._expanded += 1 # DO NOT CHANGE
+        if state not in self._visited:
+            self._visited[state] = True
+            self._visitedlist.append(state)
+
+        return successors
+
+    def getCostOfActions(self, actions):
+        """
+        Returns the cost of a particular sequence of actions. If those actions
+        include an illegal move, return 999999.
+        """
+        if actions == None: return 999999
+        x,y= self.getStartState()
+        cost = 0
+        for action in actions:
+            # Check figure out the next state and see whether its' legal
+            dx, dy = Actions.directionToVector(action)
+            x, y = int(x + dx), int(y + dy)
+            if self.walls[x][y]: return 999999
+            cost += self.costFn((x,y))
+        return cost
+
+class StayEastSearchAgent(SearchAgent):
+    """
+    An agent for position search with a cost function that penalizes being in
+    positions on the West side of the board.
+
+    The cost function for stepping into a position (x,y) is 1/2^x.
+    """
+    def __init__(self):
+        self.searchFunction = search.uniformCostSearch
+        costFn = lambda pos: .5 ** pos[0]
+        self.searchType = lambda state: PositionSearchProblem(state, costFn, (1, 1), None, False)
+
+class StayWestSearchAgent(SearchAgent):
+    """
+    An agent for position search with a cost function that penalizes being in
+    positions on the East side of the board.
+
+    The cost function for stepping into a position (x,y) is 2^x.
+    """
+    def __init__(self):
+        self.searchFunction = search.uniformCostSearch
+        costFn = lambda pos: 2 ** pos[0]
+        self.searchType = lambda state: PositionSearchProblem(state, costFn)
+
+def manhattanHeuristic(position, problem, info={}):
+    "The Manhattan distance heuristic for a PositionSearchProblem"
+    xy1 = position
+    xy2 = problem.goal
+    return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])
+
+def euclideanHeuristic(position, problem, info={}):
+    "The Euclidean distance heuristic for a PositionSearchProblem"
+    xy1 = position
+    xy2 = problem.goal
+    return ( (xy1[0] - xy2[0]) ** 2 + (xy1[1] - xy2[1]) ** 2 ) ** 0.5
+
+#####################################################
+# This portion is incomplete.  Time to write code!  #
+#####################################################
+
+class CornersProblem(search.SearchProblem):
+    """
+    This search problem finds paths through all four corners of a layout.
+
+    You must select a suitable state space and successor function
+    """
+
+    def __init__(self, startingGameState):
+        """
+        Stores the walls, pacman's starting position and corners.
+        """
+        self.walls = startingGameState.getWalls()
+        self.startingPosition = startingGameState.getPacmanPosition()
+        top, right = self.walls.height-2, self.walls.width-2
+        self.corners = ((1,1), (1,top), (right, 1), (right, top))
+        for corner in self.corners:
+            if not startingGameState.hasFood(*corner):
+                print 'Warning: no food in corner ' + str(corner)
+        self._expanded = 0 # DO NOT CHANGE; Number of search nodes expanded
+        # Please add any code here which you would like to use
+        # in initializing the problem
+        "*** YOUR CODE HERE ***"
+        self.top = top
+        self.right = right
+
+    def getStartState(self):
+        """
+        Returns the start state (in your state space, not the full Pacman state
+        space)
+        """
+        "*** YOUR CODE HERE ***"
+        allCorners = (False,False,False,False)
+        startState = (self.startingPosition,allCorners)
+        return startState
+        util.raiseNotDefined()
+
+    def isGoalState(self, state):
+        """
+        Returns whether this search state is a goal state of the problem.
+        """
+        "*** YOUR CODE HERE ***"
+        Corners = state[1]
+        return Corners[0] and Corners[1] and Corners[2] and Corners[3]
+        util.raiseNotDefined()
+
+    def getSuccessors(self, state):
+        """
+        Returns successor states, the actions they require, and a cost of 1.
+
+         As noted in search.py:
+            For a given state, this should return a list of triples, (successor,
+            action, stepCost), where 'successor' is a successor to the current
+            state, 'action' is the action required to get there, and 'stepCost'
+            is the incremental cost of expanding to that successor
+        """
+
+        successors = []
+        for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:
+            # Add a successor state to the successor list if the action is legal
+            # Here's a code snippet for figuring out whether a new position hits a wall:
+            #   x,y = currentPosition
+            #   dx, dy = Actions.directionToVector(action)
+            #   nextx, nexty = int(x + dx), int(y + dy)
+            #   hitsWall = self.walls[nextx][nexty]
+
+            "*** YOUR CODE HERE ***"
+            x,y = state[0]
+            Corners = state[1]
+            Corners_0 = Corners[0]
+            Corners_1 = Corners[1]
+            Corners_2 = Corners[2]
+            Corners_3 = Corners[3]
+            dx , dy = Actions.directionToVector(action)
+            nextx, nexty = int(x + dx), int(y + dy)
+            hitsWall = self.walls[nextx][nexty]
+            newCorners = []
+            newState = (nextx,nexty)
+            if not hitsWall:
+                if newState in self.corners:
+                    if newState == (1,1):
+                        newCorners = [True,Corners_1,Corners_2,Corners_3]
+                    elif newState == (1,self.top):
+                        newCorners = [Corners_0,True,Corners_2,Corners_3]
+                    elif newState == (self.right,1):
+                        newCorners = [Corners_0,Corners_1,True,Corners_3]
+                    elif newState == (self.right,self.top):
+                        newCorners = [Corners_0,Corners_1,Corners_2,True]
+                    successor =((newState,newCorners),action,1)
+                else:
+                    successor = ((newState,Corners),action,1)
+                successors.append(successor)
+        self._expanded += 1 # DO NOT CHANGE
+        return successors
+
+    def getCostOfActions(self, actions):
+        """
+        Returns the cost of a particular sequence of actions.  If those actions
+        include an illegal move, return 999999.  This is implemented for you.
+        """
+        if actions == None: return 999999
+        x,y= self.startingPosition
+        for action in actions:
+            dx, dy = Actions.directionToVector(action)
+            x, y = int(x + dx), int(y + dy)
+            if self.walls[x][y]: return 999999
+        return len(actions)
+
+
+def cornersHeuristic(state, problem):
+    """
+    A heuristic for the CornersProblem that you defined.
+
+      state:   The current search state
+               (a data structure you chose in your search problem)
+
+      problem: The CornersProblem instance for this layout.
+
+    This function should always return a number that is a lower bound on the
+    shortest path from the state to a goal of the problem; i.e.  it should be
+    admissible (as well as consistent).
+    """
+    corners = problem.corners # These are the corner coordinates
+    walls = problem.walls # These are the walls of the maze, as a Grid (game.py)
+
+    "*** YOUR CODE HERE ***"
+    currentPosition = state[0]
+    stateCorners = state[1]
+    top = walls.height - 2
+    right = walls.width - 2
+    Node = []
+    for i in corners:
+        if i == (1,1):
+            if not stateCorners[0]:
+                Node.append(i)
+        elif i == (1,top):
+            if not stateCorners[1]:
+                Node.append(i)
+        elif i == (right,top):
+            if not stateCorners[2]:
+                Node.append(i)
+        elif i == (right,1):
+            if not stateCorners[3]:
+                Node.append(i)
+    cost = 0
+    position = currentPosition
+    while len(Node) > 0:
+        distArr = []
+        for i in range(0,len(Node)):
+            dist = util.manhattanDistance(position,Node[i])
+            distArr.append(dist)
+        miniDist = min(distArr)
+        cost += miniDist
+        miniDist_I = distArr.index(miniDist)
+        position = Node[miniDist_I]
+        del Node[miniDist_I]
+    return cost # Default to trivial solution
+
+class AStarCornersAgent(SearchAgent):
+    "A SearchAgent for FoodSearchProblem using A* and your foodHeuristic"
+    def __init__(self):
+        self.searchFunction = lambda prob: search.aStarSearch(prob, cornersHeuristic)
+        self.searchType = CornersProblem
+
+class FoodSearchProblem:
+    """
+    A search problem associated with finding the a path that collects all of the
+    food (dots) in a Pacman game.
+
+    A search state in this problem is a tuple ( pacmanPosition, foodGrid ) where
+      pacmanPosition: a tuple (x,y) of integers specifying Pacman's position
+      foodGrid:       a Grid (see game.py) of either True or False, specifying remaining food
+    """
+    def __init__(self, startingGameState):
+        self.start = (startingGameState.getPacmanPosition(), startingGameState.getFood())
+        self.walls = startingGameState.getWalls()
+        self.startingGameState = startingGameState
+        self._expanded = 0 # DO NOT CHANGE
+        self.heuristicInfo = {} # A dictionary for the heuristic to store information
+
+    def getStartState(self):
+        return self.start
+
+    def isGoalState(self, state):
+        return state[1].count() == 0
+
+    def getSuccessors(self, state):
+        "Returns successor states, the actions they require, and a cost of 1."
+        successors = []
+        self._expanded += 1 # DO NOT CHANGE
+        for direction in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:
+            x,y = state[0]
+            dx, dy = Actions.directionToVector(direction)
+            nextx, nexty = int(x + dx), int(y + dy)
+            if not self.walls[nextx][nexty]:
+                nextFood = state[1].copy()
+                nextFood[nextx][nexty] = False
+                successors.append( ( ((nextx, nexty), nextFood), direction, 1) )
+        return successors
+
+    def getCostOfActions(self, actions):
+        """Returns the cost of a particular sequence of actions.  If those actions
+        include an illegal move, return 999999"""
+        x,y= self.getStartState()[0]
+        cost = 0
+        for action in actions:
+            # figure out the next state and see whether it's legal
+            dx, dy = Actions.directionToVector(action)
+            x, y = int(x + dx), int(y + dy)
+            if self.walls[x][y]:
+                return 999999
+            cost += 1
+        return cost
+
+class AStarFoodSearchAgent(SearchAgent):
+    "A SearchAgent for FoodSearchProblem using A* and your foodHeuristic"
+    def __init__(self):
+        self.searchFunction = lambda prob: search.aStarSearch(prob, foodHeuristic)
+        self.searchType = FoodSearchProblem
+
+def foodHeuristic(state, problem):
+    """
+    Your heuristic for the FoodSearchProblem goes here.
+
+    This heuristic must be consistent to ensure correctness.  First, try to come
+    up with an admissible heuristic; almost all admissible heuristics will be
+    consistent as well.
+
+    If using A* ever finds a solution that is worse uniform cost search finds,
+    your heuristic is *not* consistent, and probably not admissible!  On the
+    other hand, inadmissible or inconsistent heuristics may find optimal
+    solutions, so be careful.
+
+    The state is a tuple ( pacmanPosition, foodGrid ) where foodGrid is a Grid
+    (see game.py) of either True or False. You can call foodGrid.asList() to get
+    a list of food coordinates instead.
+
+    If you want access to info like walls, capsules, etc., you can query the
+    problem.  For example, problem.walls gives you a Grid of where the walls
+    are.
+
+    If you want to *store* information to be reused in other calls to the
+    heuristic, there is a dictionary called problem.heuristicInfo that you can
+    use. For example, if you only want to count the walls once and store that
+    value, try: problem.heuristicInfo['wallCount'] = problem.walls.count()
+    Subsequent calls to this heuristic can access
+    problem.heuristicInfo['wallCount']
+    """
+    position, foodGrid = state
+    "*** YOUR CODE HERE ***"
+    hvalue = 0
+    foodAvailable = []
+    totalDistance = 0
+    for i in range(0,foodGrid.width):
+        for j in range(0,foodGrid.height):
+            if foodGrid[i][j] == True:
+                foodLocation = (i,j)
+                foodAvailable.append(foodLocation)
+    if len(foodAvailable) == 0:
+        return 0
+    maxDistance = ((0,0),(0,0),0)
+    for currentFood in foodAvailable:
+        for selectFood in foodAvailable:
+            if currentFood == selectFood:
+                pass
+            else:
+                distance = util.manhattanDistance(currentFood,selectFood)
+                if maxDistance[2] < distance:
+                    maxDistance = (currentFood,selectFood,distance)
+    if maxDistance[0] == (0,0) and maxDistance[1] == (0,0):
+        hvalue = util.manhattanDistance(position,foodAvailable[0])
+    else:
+        dis_1 = util.manhattanDistance(position,maxDistance[0])
+        dis_2 = util.manhattanDistance(position,maxDistance[1])
+        hvalue = min(dis_1,dis_2) + maxDistance[2]
+    return hvalue
+
+class ClosestDotSearchAgent(SearchAgent):
+    "Search for all food using a sequence of searches"
+    def registerInitialState(self, state):
+        self.actions = []
+        currentState = state
+        while(currentState.getFood().count() > 0):
+            nextPathSegment = self.findPathToClosestDot(currentState) # The missing piece
+            self.actions += nextPathSegment
+            for action in nextPathSegment:
+                legal = currentState.getLegalActions()
+                if action not in legal:
+                    t = (str(action), str(currentState))
+                    raise Exception, 'findPathToClosestDot returned an illegal move: %s!\n%s' % t
+                currentState = currentState.generateSuccessor(0, action)
+        self.actionIndex = 0
+        print 'Path found with cost %d.' % len(self.actions)
+
+    def findPathToClosestDot(self, gameState):
+        """
+        Returns a path (a list of actions) to the closest dot, starting from
+        gameState.
+        """
+        # Here are some useful elements of the startState
+        startPosition = gameState.getPacmanPosition()
+        food = gameState.getFood()
+        walls = gameState.getWalls()
+        problem = AnyFoodSearchProblem(gameState)
+
+        "*** YOUR CODE HERE ***"
+        Result = []
+        Visited = []
+        Heap = util.PriorityQueue()
+        startState = (problem.getStartState(),[],0)
+        Heap.push(startState,startState[2])
+        while not Heap.isEmpty():
+            (state,path,cost) = Heap.pop()
+            if problem.isGoalState(state):
+                Result = path
+                break
+            if state not in Visited:
+                Visited.append(state)
+                for currentState,currentPath,currentCost in problem.getSuccessors(state):
+                    newPath = path + [currentPath]
+                    newCost = cost + currentCost
+                    newState = (currentState,newPath,newCost)
+                    Heap.push(newState,newCost)
+        return Result
+        util.raiseNotDefined()
+
+class AnyFoodSearchProblem(PositionSearchProblem):
+    """
+    A search problem for finding a path to any food.
+
+    This search problem is just like the PositionSearchProblem, but has a
+    different goal test, which you need to fill in below.  The state space and
+    successor function do not need to be changed.
+
+    The class definition above, AnyFoodSearchProblem(PositionSearchProblem),
+    inherits the methods of the PositionSearchProblem.
+
+    You can use this search problem to help you fill in the findPathToClosestDot
+    method.
+    """
+
+    def __init__(self, gameState):
+        "Stores information from the gameState.  You don't need to change this."
+        # Store the food for later reference
+        self.food = gameState.getFood()
+
+        # Store info for the PositionSearchProblem (no need to change this)
+        self.walls = gameState.getWalls()
+        self.startState = gameState.getPacmanPosition()
+        self.costFn = lambda x: 1
+        self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE
+
+    def isGoalState(self, state):
+        """
+        The state is Pacman's position. Fill this in with a goal test that will
+        complete the problem definition.
+        """
+        x,y = state
+
+        "*** YOUR CODE HERE ***"
+
+        foodGrid = self.food
+        if foodGrid[x][y] == True or foodGrid.count() == 0:
+            return True
+        else:
+            return False
+        util.raiseNotDefined()
+
+
+
+def mazeDistance(point1, point2, gameState):
+    """
+    Returns the maze distance between any two points, using the search functions
+    you have already built. The gameState can be any game state -- Pacman's
+    position in that state is ignored.
+
+    Example usage: mazeDistance( (2,4), (5,6), gameState)
+
+    This might be a useful helper function for your ApproximateSearchAgent.
+    """
+    x1, y1 = point1
+    x2, y2 = point2
+    walls = gameState.getWalls()
+    assert not walls[x1][y1], 'point1 is a wall: ' + str(point1)
+    assert not walls[x2][y2], 'point2 is a wall: ' + str(point2)
+    prob = PositionSearchProblem(gameState, start=point1, goal=point2, warn=False, visualize=False)
+    return len(search.bfs(prob))

search/searchTestClasses.py

@@ -0,0 +1,821 @@
+# searchTestClasses.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 testClasses
+import textwrap
+
+# import project specific code
+import layout
+import pacman
+from search import SearchProblem
+
+# helper function for printing solutions in solution files
+def wrap_solution(solution):
+    if type(solution) == type([]):
+        return '\n'.join(textwrap.wrap(' '.join(solution)))
+    else:
+        return str(solution)
+
+
+
+
+def followAction(state, action, problem):
+  for successor1, action1, cost1 in problem.getSuccessors(state):
+    if action == action1: return successor1
+  return None
+
+def followPath(path, problem):
+  state = problem.getStartState()
+  states = [state]
+  for action in path:
+    state = followAction(state, action, problem)
+    states.append(state)
+  return states
+
+def checkSolution(problem, path):
+  state = problem.getStartState()
+  for action in path:
+    state = followAction(state, action, problem)
+  return problem.isGoalState(state)
+
+# Search problem on a plain graph
+class GraphSearch(SearchProblem):
+
+    # Read in the state graph; define start/end states, edges and costs
+    def __init__(self, graph_text):
+        self.expanded_states = []
+        lines = graph_text.split('\n')
+        r = re.match('start_state:(.*)', lines[0])
+        if r == None:
+            print "Broken graph:"
+            print '"""%s"""' % graph_text
+            raise Exception("GraphSearch graph specification start_state not found or incorrect on line:" + l)
+        self.start_state = r.group(1).strip()
+        r = re.match('goal_states:(.*)', lines[1])
+        if r == None:
+            print "Broken graph:"
+            print '"""%s"""' % graph_text
+            raise Exception("GraphSearch graph specification goal_states not found or incorrect on line:" + l)
+        goals = r.group(1).split()
+        self.goals = map(str.strip, goals)
+        self.successors = {}
+        all_states = set()
+        self.orderedSuccessorTuples = []
+        for l in lines[2:]:
+            if len(l.split()) == 3:
+                start, action, next_state = l.split()
+                cost = 1
+            elif len(l.split()) == 4:
+                start, action, next_state, cost = l.split()
+            else:
+                print "Broken graph:"
+                print '"""%s"""' % graph_text
+                raise Exception("Invalid line in GraphSearch graph specification on line:" + l)
+            cost = float(cost)
+            self.orderedSuccessorTuples.append((start, action, next_state, cost))
+            all_states.add(start)
+            all_states.add(next_state)
+            if start not in self.successors:
+                self.successors[start] = []
+            self.successors[start].append((next_state, action, cost))
+        for s in all_states:
+            if s not in self.successors:
+                self.successors[s] = []
+
+    # Get start state
+    def getStartState(self):
+        return self.start_state
+
+    # Check if a state is a goal state
+    def isGoalState(self, state):
+        return state in self.goals
+
+    # Get all successors of a state
+    def getSuccessors(self, state):
+        self.expanded_states.append(state)
+        return list(self.successors[state])
+
+    # Calculate total cost of a sequence of actions
+    def getCostOfActions(self, actions):
+        total_cost = 0
+        state = self.start_state
+        for a in actions:
+            successors = self.successors[state]
+            match = False
+            for (next_state, action, cost) in successors:
+                if a == action:
+                    state = next_state
+                    total_cost += cost
+                    match = True
+            if not match:
+                print 'invalid action sequence'
+                sys.exit(1)
+        return total_cost
+
+    # Return a list of all states on which 'getSuccessors' was called
+    def getExpandedStates(self):
+        return self.expanded_states
+
+    def __str__(self):
+        print self.successors
+        edges = ["%s %s %s %s" % t for t in self.orderedSuccessorTuples]
+        return \
+"""start_state: %s
+goal_states: %s
+%s""" % (self.start_state, " ".join(self.goals), "\n".join(edges))
+
+
+
+def parseHeuristic(heuristicText):
+    heuristic = {}
+    for line in heuristicText.split('\n'):
+        tokens = line.split()
+        if len(tokens) != 2:
+            print "Broken heuristic:"
+            print '"""%s"""' % graph_text
+            raise Exception("GraphSearch heuristic specification broken:" + l)
+        state, h = tokens
+        heuristic[state] = float(h)
+
+    def graphHeuristic(state, problem=None):
+        if state in heuristic:
+            return heuristic[state]
+        else:
+            pp = pprint.PrettyPrinter(indent=4)
+            print "Heuristic:"
+            pp.pprint(heuristic)
+            raise Exception("Graph heuristic called with invalid state: " + str(state))
+
+    return graphHeuristic
+
+
+class GraphSearchTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(GraphSearchTest, self).__init__(question, testDict)
+        self.graph_text = testDict['graph']
+        self.alg = testDict['algorithm']
+        self.diagram = testDict['diagram']
+        self.exactExpansionOrder = testDict.get('exactExpansionOrder', 'True').lower() == "true"
+        if 'heuristic' in testDict:
+            self.heuristic = parseHeuristic(testDict['heuristic'])
+        else:
+            self.heuristic = None
+
+    # Note that the return type of this function is a tripple:
+    # (solution, expanded states, error message)
+    def getSolInfo(self, search):
+        alg = getattr(search, self.alg)
+        problem = GraphSearch(self.graph_text)
+        if self.heuristic != None:
+            solution = alg(problem, self.heuristic)
+        else:
+            solution = alg(problem)
+
+        if type(solution) != type([]):
+            return None, None, 'The result of %s must be a list. (Instead, it is %s)' % (self.alg, type(solution))
+
+        return solution, problem.getExpandedStates(), None
+
+    # Run student code.  If an error message is returned, print error and return false.
+    # If a good solution is returned, printn the solution and return true; otherwise,
+    # print both the correct and student's solution and return false.
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        gold_solution = [str.split(solutionDict['solution']), str.split(solutionDict['rev_solution'])]
+        gold_expanded_states = [str.split(solutionDict['expanded_states']), str.split(solutionDict['rev_expanded_states'])]
+
+        solution, expanded_states, error = self.getSolInfo(search)
+        if error != None:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('\t%s' % error)
+            return False
+
+        if solution in gold_solution and (not self.exactExpansionOrder or expanded_states in gold_expanded_states):
+            grades.addMessage('PASS: %s' % self.path)
+            grades.addMessage('\tsolution:\t\t%s' % solution)
+            grades.addMessage('\texpanded_states:\t%s' % expanded_states)
+            return True
+        else:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('\tgraph:')
+            for line in self.diagram.split('\n'):
+                grades.addMessage('\t    %s' % (line,))
+            grades.addMessage('\tstudent solution:\t\t%s' % solution)
+            grades.addMessage('\tstudent expanded_states:\t%s' % expanded_states)
+            grades.addMessage('')
+            grades.addMessage('\tcorrect solution:\t\t%s' % gold_solution[0])
+            grades.addMessage('\tcorrect expanded_states:\t%s' % gold_expanded_states[0])
+            grades.addMessage('\tcorrect rev_solution:\t\t%s' % gold_solution[1])
+            grades.addMessage('\tcorrect rev_expanded_states:\t%s' % gold_expanded_states[1])
+            return False
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # open file and write comments
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+        handle.write('# This solution is designed to support both right-to-left\n')
+        handle.write('# and left-to-right implementations.\n')
+
+        # write forward solution
+        solution, expanded_states, error = self.getSolInfo(search)
+        if error != None: raise Exception("Error in solution code: %s" % error)
+        handle.write('solution: "%s"\n' % ' '.join(solution))
+        handle.write('expanded_states: "%s"\n' % ' '.join(expanded_states))
+
+        # reverse and write backwards solution
+        search.REVERSE_PUSH = not search.REVERSE_PUSH
+        solution, expanded_states, error = self.getSolInfo(search)
+        if error != None: raise Exception("Error in solution code: %s" % error)
+        handle.write('rev_solution: "%s"\n' % ' '.join(solution))
+        handle.write('rev_expanded_states: "%s"\n' % ' '.join(expanded_states))
+
+        # clean up
+        search.REVERSE_PUSH = not search.REVERSE_PUSH
+        handle.close()
+        return True
+
+
+
+class PacmanSearchTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(PacmanSearchTest, self).__init__(question, testDict)
+        self.layout_text = testDict['layout']
+        self.alg = testDict['algorithm']
+        self.layoutName = testDict['layoutName']
+
+        # TODO: sensible to have defaults like this?
+        self.leewayFactor = float(testDict.get('leewayFactor', '1'))
+        self.costFn = eval(testDict.get('costFn', 'None'))
+        self.searchProblemClassName = testDict.get('searchProblemClass', 'PositionSearchProblem')
+        self.heuristicName = testDict.get('heuristic', None)
+
+
+    def getSolInfo(self, search, searchAgents):
+        alg = getattr(search, self.alg)
+        lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
+        start_state = pacman.GameState()
+        start_state.initialize(lay, 0)
+
+        problemClass = getattr(searchAgents, self.searchProblemClassName)
+        problemOptions = {}
+        if self.costFn != None:
+            problemOptions['costFn'] = self.costFn
+        problem = problemClass(start_state, **problemOptions)
+        heuristic = getattr(searchAgents, self.heuristicName) if self.heuristicName != None else None
+
+        if heuristic != None:
+            solution = alg(problem, heuristic)
+        else:
+            solution = alg(problem)
+
+        if type(solution) != type([]):
+            return None, None, 'The result of %s must be a list. (Instead, it is %s)' % (self.alg, type(solution))
+
+        from game import Directions
+        dirs = Directions.LEFT.keys()
+        if [el in dirs for el in solution].count(False) != 0:
+            return None, None, 'Output of %s must be a list of actions from game.Directions' % self.alg
+
+        expanded = problem._expanded
+        return solution, expanded, None
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        gold_solution = [str.split(solutionDict['solution']), str.split(solutionDict['rev_solution'])]
+        gold_expanded = max(int(solutionDict['expanded_nodes']), int(solutionDict['rev_expanded_nodes']))
+
+        solution, expanded, error = self.getSolInfo(search, searchAgents)
+        if error != None:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('%s' % error)
+            return False
+
+        # FIXME: do we want to standardize test output format?
+
+        if solution not in gold_solution:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('Solution not correct.')
+            grades.addMessage('\tstudent solution length: %s' % len(solution))
+            grades.addMessage('\tstudent solution:\n%s' % wrap_solution(solution))
+            grades.addMessage('')
+            grades.addMessage('\tcorrect solution length: %s' % len(gold_solution[0]))
+            grades.addMessage('\tcorrect (reversed) solution length: %s' % len(gold_solution[1]))
+            grades.addMessage('\tcorrect solution:\n%s' % wrap_solution(gold_solution[0]))
+            grades.addMessage('\tcorrect (reversed) solution:\n%s' % wrap_solution(gold_solution[1]))
+            return False
+
+        if expanded > self.leewayFactor * gold_expanded and expanded > gold_expanded + 1:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('Too many node expanded; are you expanding nodes twice?')
+            grades.addMessage('\tstudent nodes expanded: %s' % expanded)
+            grades.addMessage('')
+            grades.addMessage('\tcorrect nodes expanded: %s (leewayFactor %s)' % (gold_expanded, self.leewayFactor))
+            return False
+
+        grades.addMessage('PASS: %s' % self.path)
+        grades.addMessage('\tpacman layout:\t\t%s' % self.layoutName)
+        grades.addMessage('\tsolution length: %s' % len(solution))
+        grades.addMessage('\tnodes expanded:\t\t%s' % expanded)
+        return True
+
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # open file and write comments
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+        handle.write('# This solution is designed to support both right-to-left\n')
+        handle.write('# and left-to-right implementations.\n')
+        handle.write('# Number of nodes expanded must be with a factor of %s of the numbers below.\n' % self.leewayFactor)
+
+        # write forward solution
+        solution, expanded, error = self.getSolInfo(search, searchAgents)
+        if error != None: raise Exception("Error in solution code: %s" % error)
+        handle.write('solution: """\n%s\n"""\n' % wrap_solution(solution))
+        handle.write('expanded_nodes: "%s"\n' % expanded)
+
+        # write backward solution
+        search.REVERSE_PUSH = not search.REVERSE_PUSH
+        solution, expanded, error = self.getSolInfo(search, searchAgents)
+        if error != None: raise Exception("Error in solution code: %s" % error)
+        handle.write('rev_solution: """\n%s\n"""\n' % wrap_solution(solution))
+        handle.write('rev_expanded_nodes: "%s"\n' % expanded)
+
+        # clean up
+        search.REVERSE_PUSH = not search.REVERSE_PUSH
+        handle.close()
+        return True
+
+
+from game import Actions
+def getStatesFromPath(start, path):
+    "Returns the list of states visited along the path"
+    vis = [start]
+    curr = start
+    for a in path:
+        x,y = curr
+        dx, dy = Actions.directionToVector(a)
+        curr = (int(x + dx), int(y + dy))
+        vis.append(curr)
+    return vis
+
+class CornerProblemTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(CornerProblemTest, self).__init__(question, testDict)
+        self.layoutText = testDict['layout']
+        self.layoutName = testDict['layoutName']
+
+    def solution(self, search, searchAgents):
+        lay = layout.Layout([l.strip() for l in self.layoutText.split('\n')])
+        gameState = pacman.GameState()
+        gameState.initialize(lay, 0)
+        problem = searchAgents.CornersProblem(gameState)
+        path = search.bfs(problem)
+
+        gameState = pacman.GameState()
+        gameState.initialize(lay, 0)
+        visited = getStatesFromPath(gameState.getPacmanPosition(), path)
+        top, right = gameState.getWalls().height-2, gameState.getWalls().width-2
+        missedCorners = [p for p in ((1,1), (1,top), (right, 1), (right, top)) if p not in visited]
+
+        return path, missedCorners
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        gold_length = int(solutionDict['solution_length'])
+        solution, missedCorners = self.solution(search, searchAgents)
+
+        if type(solution) != type([]):
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('The result must be a list. (Instead, it is %s)' % type(solution))
+            return False
+
+        if len(missedCorners) != 0:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('Corners missed: %s' % missedCorners)
+            return False
+
+        if len(solution) != gold_length:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('Optimal solution not found.')
+            grades.addMessage('\tstudent solution length:\n%s' % len(solution))
+            grades.addMessage('')
+            grades.addMessage('\tcorrect solution length:\n%s' % gold_length)
+            return False
+
+        grades.addMessage('PASS: %s' % self.path)
+        grades.addMessage('\tpacman layout:\t\t%s' % self.layoutName)
+        grades.addMessage('\tsolution length:\t\t%s' % len(solution))
+        return True
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # open file and write comments
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+
+        print "Solving problem", self.layoutName
+        print self.layoutText
+
+        path, _ = self.solution(search, searchAgents)
+        length = len(path)
+        print "Problem solved"
+
+        handle.write('solution_length: "%s"\n' % length)
+        handle.close()
+
+
+
+
+# template = """class: "HeuristicTest"
+#
+# heuristic: "foodHeuristic"
+# searchProblemClass: "FoodSearchProblem"
+# layoutName: "Test %s"
+# layout: \"\"\"
+# %s
+# \"\"\"
+# """
+#
+# for i, (_, _, l) in enumerate(doneTests + foodTests):
+#     f = open("food_heuristic_%s.test" % (i+1), "w")
+#     f.write(template % (i+1, "\n".join(l)))
+#     f.close()
+
+class HeuristicTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(HeuristicTest, self).__init__(question, testDict)
+        self.layoutText = testDict['layout']
+        self.layoutName = testDict['layoutName']
+        self.searchProblemClassName = testDict['searchProblemClass']
+        self.heuristicName = testDict['heuristic']
+
+    def setupProblem(self, searchAgents):
+        lay = layout.Layout([l.strip() for l in self.layoutText.split('\n')])
+        gameState = pacman.GameState()
+        gameState.initialize(lay, 0)
+        problemClass = getattr(searchAgents, self.searchProblemClassName)
+        problem = problemClass(gameState)
+        state = problem.getStartState()
+        heuristic = getattr(searchAgents, self.heuristicName)
+
+        return problem, state, heuristic
+
+    def checkHeuristic(self, heuristic, problem, state, solutionCost):
+        h0 = heuristic(state, problem)
+
+        if solutionCost == 0:
+            if h0 == 0:
+                return True, ''
+            else:
+                return False, 'Heuristic failed H(goal) == 0 test'
+
+        if h0 < 0:
+            return False, 'Heuristic failed H >= 0 test'
+        if not h0 > 0:
+            return False, 'Heuristic failed non-triviality test'
+        if not h0 <= solutionCost:
+            return False, 'Heuristic failed admissibility test'
+
+        for succ, action, stepCost in problem.getSuccessors(state):
+            h1 = heuristic(succ, problem)
+            if h1 < 0: return False, 'Heuristic failed H >= 0 test'
+            if h0 - h1 > stepCost: return False, 'Heuristic failed consistency test'
+
+        return True, ''
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        solutionCost = int(solutionDict['solution_cost'])
+        problem, state, heuristic = self.setupProblem(searchAgents)
+
+        passed, message = self.checkHeuristic(heuristic, problem, state, solutionCost)
+
+        if not passed:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('%s' % message)
+            return False
+        else:
+            grades.addMessage('PASS: %s' % self.path)
+            return True
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # open file and write comments
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+
+        print "Solving problem", self.layoutName, self.heuristicName
+        print self.layoutText
+        problem, _, heuristic = self.setupProblem(searchAgents)
+        path = search.astar(problem, heuristic)
+        cost = problem.getCostOfActions(path)
+        print "Problem solved"
+
+        handle.write('solution_cost: "%s"\n' % cost)
+        handle.close()
+        return True
+
+
+
+
+
+
+class HeuristicGrade(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(HeuristicGrade, self).__init__(question, testDict)
+        self.layoutText = testDict['layout']
+        self.layoutName = testDict['layoutName']
+        self.searchProblemClassName = testDict['searchProblemClass']
+        self.heuristicName = testDict['heuristic']
+        self.basePoints = int(testDict['basePoints'])
+        self.thresholds = [int(t) for t in testDict['gradingThresholds'].split()]
+
+    def setupProblem(self, searchAgents):
+        lay = layout.Layout([l.strip() for l in self.layoutText.split('\n')])
+        gameState = pacman.GameState()
+        gameState.initialize(lay, 0)
+        problemClass = getattr(searchAgents, self.searchProblemClassName)
+        problem = problemClass(gameState)
+        state = problem.getStartState()
+        heuristic = getattr(searchAgents, self.heuristicName)
+
+        return problem, state, heuristic
+
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        problem, _, heuristic = self.setupProblem(searchAgents)
+
+        path = search.astar(problem, heuristic)
+
+        expanded = problem._expanded
+
+        if not checkSolution(problem, path):
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('\tReturned path is not a solution.')
+            grades.addMessage('\tpath returned by astar: %s' % expanded)
+            return False
+
+        grades.addPoints(self.basePoints)
+        points = 0
+        for threshold in self.thresholds:
+            if expanded <= threshold:
+                points += 1
+        grades.addPoints(points)
+        if points >= len(self.thresholds):
+            grades.addMessage('PASS: %s' % self.path)
+        else:
+            grades.addMessage('FAIL: %s' % self.path)
+        grades.addMessage('\texpanded nodes: %s' % expanded)
+        grades.addMessage('\tthresholds: %s' % self.thresholds)
+
+        return True
+
+
+    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
+
+
+
+
+
+# template = """class: "ClosestDotTest"
+#
+# layoutName: "Test %s"
+# layout: \"\"\"
+# %s
+# \"\"\"
+# """
+#
+# for i, (_, _, l) in enumerate(foodTests):
+#     f = open("closest_dot_%s.test" % (i+1), "w")
+#     f.write(template % (i+1, "\n".join(l)))
+#     f.close()
+
+class ClosestDotTest(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(ClosestDotTest, self).__init__(question, testDict)
+        self.layoutText = testDict['layout']
+        self.layoutName = testDict['layoutName']
+
+    def solution(self, searchAgents):
+        lay = layout.Layout([l.strip() for l in self.layoutText.split('\n')])
+        gameState = pacman.GameState()
+        gameState.initialize(lay, 0)
+        path = searchAgents.ClosestDotSearchAgent().findPathToClosestDot(gameState)
+        return path
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        gold_length = int(solutionDict['solution_length'])
+        solution = self.solution(searchAgents)
+
+        if type(solution) != type([]):
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('\tThe result must be a list. (Instead, it is %s)' % type(solution))
+            return False
+
+        if len(solution) != gold_length:
+            grades.addMessage('FAIL: %s' % self.path)
+            grades.addMessage('Closest dot not found.')
+            grades.addMessage('\tstudent solution length:\n%s' % len(solution))
+            grades.addMessage('')
+            grades.addMessage('\tcorrect solution length:\n%s' % gold_length)
+            return False
+
+        grades.addMessage('PASS: %s' % self.path)
+        grades.addMessage('\tpacman layout:\t\t%s' % self.layoutName)
+        grades.addMessage('\tsolution length:\t\t%s' % len(solution))
+        return True
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # open file and write comments
+        handle = open(filePath, 'w')
+        handle.write('# This is the solution file for %s.\n' % self.path)
+
+        print "Solving problem", self.layoutName
+        print self.layoutText
+
+        length = len(self.solution(searchAgents))
+        print "Problem solved"
+
+        handle.write('solution_length: "%s"\n' % length)
+        handle.close()
+        return True
+
+
+
+
+class CornerHeuristicSanity(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(CornerHeuristicSanity, self).__init__(question, testDict)
+        self.layout_text = testDict['layout']
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        game_state = pacman.GameState()
+        lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
+        game_state.initialize(lay, 0)
+        problem = searchAgents.CornersProblem(game_state)
+        start_state = problem.getStartState()
+        h0 = searchAgents.cornersHeuristic(start_state, problem)
+        succs = problem.getSuccessors(start_state)
+        # cornerConsistencyA
+        for succ in succs:
+            h1 = searchAgents.cornersHeuristic(succ[0], problem)
+            if h0 - h1 > 1:
+                grades.addMessage('FAIL: inconsistent heuristic')
+                return False
+        heuristic_cost = searchAgents.cornersHeuristic(start_state, problem)
+        true_cost = float(solutionDict['cost'])
+        # cornerNontrivial
+        if heuristic_cost == 0:
+            grades.addMessage('FAIL: must use non-trivial heuristic')
+            return False
+        # cornerAdmissible
+        if heuristic_cost > true_cost:
+            grades.addMessage('FAIL: Inadmissible heuristic')
+            return False
+        path = solutionDict['path'].split()
+        states = followPath(path, problem)
+        heuristics = []
+        for state in states:
+            heuristics.append(searchAgents.cornersHeuristic(state, problem))
+        for i in range(0, len(heuristics) - 1):
+            h0 = heuristics[i]
+            h1 = heuristics[i+1]
+            # cornerConsistencyB
+            if h0 - h1 > 1:
+                grades.addMessage('FAIL: inconsistent heuristic')
+                return False
+            # cornerPosH
+            if h0 < 0 or h1 <0:
+                grades.addMessage('FAIL: non-positive heuristic')
+                return False
+        # cornerGoalH
+        if heuristics[len(heuristics) - 1] != 0:
+            grades.addMessage('FAIL: heuristic non-zero at goal')
+            return False
+        grades.addMessage('PASS: heuristic value less than true cost at start state')
+        return True
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # write comment
+        handle = open(filePath, 'w')
+        handle.write('# In order for a heuristic to be admissible, the value\n')
+        handle.write('# of the heuristic must be less at each state than the\n')
+        handle.write('# true cost of the optimal path from that state to a goal.\n')
+
+        # solve problem and write solution
+        lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
+        start_state = pacman.GameState()
+        start_state.initialize(lay, 0)
+        problem = searchAgents.CornersProblem(start_state)
+        solution = search.astar(problem, searchAgents.cornersHeuristic)
+        handle.write('cost: "%d"\n' % len(solution))
+        handle.write('path: """\n%s\n"""\n' % wrap_solution(solution))
+        handle.close()
+        return True
+
+
+
+class CornerHeuristicPacman(testClasses.TestCase):
+
+    def __init__(self, question, testDict):
+        super(CornerHeuristicPacman, self).__init__(question, testDict)
+        self.layout_text = testDict['layout']
+
+    def execute(self, grades, moduleDict, solutionDict):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        total = 0
+        true_cost = float(solutionDict['cost'])
+        thresholds = map(int, solutionDict['thresholds'].split())
+        game_state = pacman.GameState()
+        lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
+        game_state.initialize(lay, 0)
+        problem = searchAgents.CornersProblem(game_state)
+        start_state = problem.getStartState()
+        if searchAgents.cornersHeuristic(start_state, problem) > true_cost:
+            grades.addMessage('FAIL: Inadmissible heuristic')
+            return False
+        path = search.astar(problem, searchAgents.cornersHeuristic)
+        print "path:", path
+        print "path length:", len(path)
+        cost = problem.getCostOfActions(path)
+        if cost > true_cost:
+            grades.addMessage('FAIL: Inconsistent heuristic')
+            return False
+        expanded = problem._expanded
+        points = 0
+        for threshold in thresholds:
+            if expanded <= threshold:
+                points += 1
+        grades.addPoints(points)
+        if points >= len(thresholds):
+            grades.addMessage('PASS: Heuristic resulted in expansion of %d nodes' % expanded)
+        else:
+            grades.addMessage('FAIL: Heuristic resulted in expansion of %d nodes' % expanded)
+        return True
+
+    def writeSolution(self, moduleDict, filePath):
+        search = moduleDict['search']
+        searchAgents = moduleDict['searchAgents']
+        # write comment
+        handle = open(filePath, 'w')
+        handle.write('# This solution file specifies the length of the optimal path\n')
+        handle.write('# as well as the thresholds on number of nodes expanded to be\n')
+        handle.write('# used in scoring.\n')
+
+        # solve problem and write solution
+        lay = layout.Layout([l.strip() for l in self.layout_text.split('\n')])
+        start_state = pacman.GameState()
+        start_state.initialize(lay, 0)
+        problem = searchAgents.CornersProblem(start_state)
+        solution = search.astar(problem, searchAgents.cornersHeuristic)
+        handle.write('cost: "%d"\n' % len(solution))
+        handle.write('path: """\n%s\n"""\n' % wrap_solution(solution))
+        handle.write('thresholds: "2000 1600 1200"\n')
+        handle.close()
+        return True
+

search/testClasses.py

@@ -0,0 +1,206 @@
+# 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'))
+

search/testParser.py

@@ -0,0 +1,85 @@
+# 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__")

search/test_cases/CONFIG

@@ -0,0 +1 @@
+order: "q1 q2 q3 q4 q5 q6 q7 q8"

search/test_cases/q1/CONFIG

@@ -0,0 +1,2 @@
+max_points: "3"
+class: "PassAllTestsQuestion"

search/test_cases/q1/graph_backtrack.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q1/graph_backtrack.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "1:A->C 0:C->G"
+expanded_states: "A D C"
+rev_solution: "1:A->C 0:C->G"
+rev_expanded_states: "A B C"

search/test_cases/q1/graph_backtrack.test

@@ -0,0 +1,32 @@
+class: "GraphSearchTest"
+algorithm: "depthFirstSearch"
+
+diagram: """
+ B   
+ ^
+ |
+*A --> C --> G
+ |
+ V
+ D
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.  This tests whether
+you extract the sequence of actions correctly even
+if your search backtracks.  If you fail this, your
+nodes are not correctly tracking the sequences of
+actions required to reach them.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+A 1:A->C C 2.0
+A 2:A->D D 4.0
+C 0:C->G G 8.0
+"""

search/test_cases/q1/graph_bfs_vs_dfs.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q1/graph_bfs_vs_dfs.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "2:A->D 0:D->G"
+expanded_states: "A D"
+rev_solution: "0:A->B 0:B->D 0:D->G"
+rev_expanded_states: "A B D"

search/test_cases/q1/graph_bfs_vs_dfs.test

@@ -0,0 +1,30 @@
+# Graph where BFS finds the optimal solution but DFS does not
+class: "GraphSearchTest"
+algorithm: "depthFirstSearch"
+
+diagram: """
+/-- B
+|   ^
+|   |
+|  *A -->[G]
+|   |     ^
+|   V     |
+\-->D ----/
+
+A is the start state, G is the goal.  Arrows
+mark possible transitions
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+A 1:A->G G 2.0
+A 2:A->D D 4.0
+B 0:B->D D 8.0
+D 0:D->G G 16.0
+"""

search/test_cases/q1/graph_infinite.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q1/graph_infinite.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "0:A->B 1:B->C 1:C->G"
+expanded_states: "A B C"
+rev_solution: "0:A->B 1:B->C 1:C->G"
+rev_expanded_states: "A B C"

search/test_cases/q1/graph_infinite.test

@@ -0,0 +1,30 @@
+# Graph where natural action choice leads to an infinite loop
+class: "GraphSearchTest"
+algorithm: "depthFirstSearch"
+
+diagram: """
+ B <--> C
+ ^     /|
+ |    / |
+ V   /  V
+*A<-/  [G]
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+B 0:B->A A 2.0
+B 1:B->C C 4.0
+C 0:C->A A 8.0
+C 1:C->G G 16.0
+C 2:C->B B 32.0
+"""
+

search/test_cases/q1/graph_manypaths.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q1/graph_manypaths.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "2:A->B2 0:B2->C 0:C->D 2:D->E2 0:E2->F 0:F->G"
+expanded_states: "A B2 C D E2 F"
+rev_solution: "0:A->B1 0:B1->C 0:C->D 0:D->E1 0:E1->F 0:F->G"
+rev_expanded_states: "A B1 C D E1 F"

search/test_cases/q1/graph_manypaths.test

@@ -0,0 +1,39 @@
+class: "GraphSearchTest"
+algorithm: "depthFirstSearch"
+
+diagram: """
+    B1          E1
+   ^  \        ^  \
+  /    V      /    V
+*A --> C --> D --> F --> [G]
+  \    ^      \    ^
+   V  /        V  /
+    B2          E2
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.  This graph has multiple
+paths to the goal, where nodes with the same state 
+are added to the fringe multiple times before they
+are expanded.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B1 B1 1.0
+A 1:A->C C 2.0
+A 2:A->B2 B2 4.0
+B1 0:B1->C C 8.0
+B2 0:B2->C C 16.0
+C 0:C->D D 32.0
+D 0:D->E1 E1 64.0
+D 1:D->F F 128.0
+D 2:D->E2 E2 256.0
+E1 0:E1->F F 512.0
+E2 0:E2->F F 1024.0
+F 0:F->G G 2048.0
+"""

search/test_cases/q1/pacman_1.solution

@@ -0,0 +1,40 @@
+# This is the solution file for test_cases/q1/pacman_1.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+# Number of nodes expanded must be with a factor of 1.0 of the numbers below.
+solution: """
+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 West
+West West West West West South South South South South South South
+South South East East East North North North North North North North
+East East South South South South South South East East North North
+North North North North East East South South South South East East
+North North East East East East East East East East South South South
+East East East East East East East South South South South South South
+South West West West West West West West West West West West West West
+West West West West South West West West West West West West West West
+"""
+expanded_nodes: "146"
+rev_solution: """
+South South West West West West South South East East East East South
+South West West West West South South East East East East South South
+West West West West South South South East North East East East South
+South South West West West West West West West North North North North
+North North North North West West West West West West West North North
+North East East East East South East East East North North North West
+West North North West West West West West West West West West West
+West West West West West West West West West West West West West West
+South South South South South South South South South East East East
+North North North North North North North East East South South South
+South South South East East North North North North North North East
+East South South South South East East North North North North East
+East East East East South South West West West South South East East
+East South South West West West West West West South South West West
+West West West South West West West West West South South East East
+East East East East East North East East East East East North North
+East East East East East East North East East East East East South
+South West West West South West West West West West West South South
+West West West West West South West West West West West West West West
+West
+"""
+rev_expanded_nodes: "269"

search/test_cases/q1/pacman_1.test

@@ -0,0 +1,27 @@
+# This is a basic depth first search test
+class: "PacmanSearchTest"
+algorithm: "depthFirstSearch"
+
+# The following specifies the layout to be used 
+layoutName: "mediumMaze"
+layout: """
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                 P%
+% %%%%%%%%%%%%%%%%%%%%%%% %%%%%%%% %
+% %%   %   %      %%%%%%%   %%     %
+% %% % % % % %%%% %%%%%%%%% %% %%%%%
+% %% % % % %             %% %%     %
+% %% % % % % % %%%%  %%%    %%%%%% %
+% %  % % %   %    %% %%%%%%%%      %
+% %% % % %%%%%%%% %%        %% %%%%%
+% %% %   %%       %%%%%%%%% %%     %
+%    %%%%%% %%%%%%%      %% %%%%%% %
+%%%%%%      %       %%%% %% %      %
+%      %%%%%% %%%%% %    %% %% %%%%%
+% %%%%%%      %       %%%%% %%     %
+%        %%%%%% %%%%%%%%%%% %%  %% %
+%%%%%%%%%%                  %%%%%% %
+%.         %%%%%%%%%%%%%%%%        %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+"""
+

search/test_cases/q2/CONFIG

@@ -0,0 +1,2 @@
+max_points: "3"
+class: "PassAllTestsQuestion"

search/test_cases/q2/graph_backtrack.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q2/graph_backtrack.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "1:A->C 0:C->G"
+expanded_states: "A B C D"
+rev_solution: "1:A->C 0:C->G"
+rev_expanded_states: "A D C B"

search/test_cases/q2/graph_backtrack.test

@@ -0,0 +1,32 @@
+class: "GraphSearchTest"
+algorithm: "breadthFirstSearch"
+
+diagram: """
+ B   
+ ^
+ |
+*A --> C --> G
+ |
+ V
+ D
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.  This tests whether
+you extract the sequence of actions correctly even
+if your search backtracks.  If you fail this, your
+nodes are not correctly tracking the sequences of
+actions required to reach them.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+A 1:A->C C 2.0
+A 2:A->D D 4.0
+C 0:C->G G 8.0
+"""

search/test_cases/q2/graph_bfs_vs_dfs.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q2/graph_bfs_vs_dfs.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "1:A->G"
+expanded_states: "A B"
+rev_solution: "1:A->G"
+rev_expanded_states: "A D"

search/test_cases/q2/graph_bfs_vs_dfs.test

@@ -0,0 +1,30 @@
+# Graph where BFS finds the optimal solution but DFS does not
+class: "GraphSearchTest"
+algorithm: "breadthFirstSearch"
+
+diagram: """
+/-- B
+|   ^
+|   |
+|  *A -->[G]
+|   |     ^
+|   V     |
+\-->D ----/
+
+A is the start state, G is the goal.  Arrows
+mark possible transitions
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+A 1:A->G G 2.0
+A 2:A->D D 4.0
+B 0:B->D D 8.0
+D 0:D->G G 16.0
+"""

search/test_cases/q2/graph_infinite.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q2/graph_infinite.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "0:A->B 1:B->C 1:C->G"
+expanded_states: "A B C"
+rev_solution: "0:A->B 1:B->C 1:C->G"
+rev_expanded_states: "A B C"

search/test_cases/q2/graph_infinite.test

@@ -0,0 +1,30 @@
+# Graph where natural action choice leads to an infinite loop
+class: "GraphSearchTest"
+algorithm: "breadthFirstSearch"
+
+diagram: """
+ B <--> C
+ ^     /|
+ |    / |
+ V   /  V
+*A<-/  [G]
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+B 0:B->A A 2.0
+B 1:B->C C 4.0
+C 0:C->A A 8.0
+C 1:C->G G 16.0
+C 2:C->B B 32.0
+"""
+

search/test_cases/q2/graph_manypaths.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q2/graph_manypaths.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "1:A->C 0:C->D 1:D->F 0:F->G"
+expanded_states: "A B1 C B2 D E1 F E2"
+rev_solution: "1:A->C 0:C->D 1:D->F 0:F->G"
+rev_expanded_states: "A B2 C B1 D E2 F E1"

search/test_cases/q2/graph_manypaths.test

@@ -0,0 +1,39 @@
+class: "GraphSearchTest"
+algorithm: "breadthFirstSearch"
+
+diagram: """
+    B1          E1
+   ^  \        ^  \
+  /    V      /    V
+*A --> C --> D --> F --> [G]
+  \    ^      \    ^
+   V  /        V  /
+    B2          E2
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.  This graph has multiple
+paths to the goal, where nodes with the same state 
+are added to the fringe multiple times before they
+are expanded.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B1 B1 1.0
+A 1:A->C C 2.0
+A 2:A->B2 B2 4.0
+B1 0:B1->C C 8.0
+B2 0:B2->C C 16.0
+C 0:C->D D 32.0
+D 0:D->E1 E1 64.0
+D 1:D->F F 128.0
+D 2:D->E2 E2 256.0
+E1 0:E1->F F 512.0
+E2 0:E2->F F 1024.0
+F 0:F->G G 2048.0
+"""

search/test_cases/q2/pacman_1.solution

@@ -0,0 +1,22 @@
+# This is the solution file for test_cases/q2/pacman_1.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+# Number of nodes expanded must be with a factor of 1.0 of the numbers below.
+solution: """
+West West West West West West West West West South South East East
+South South South West West West North West West West West South South
+South East East East East East East East South South South South South
+South South West West West West West West West West West West West
+West West West West West West South West West West West West West West
+West West
+"""
+expanded_nodes: "269"
+rev_solution: """
+West West West West West West West West West South South East East
+South South South West West West North West West West West South South
+South East East East East East East East South South South South South
+South South West West West West West West West West West West West
+West West West West West West South West West West West West West West
+West West
+"""
+rev_expanded_nodes: "269"

search/test_cases/q2/pacman_1.test

@@ -0,0 +1,27 @@
+# This is a basic breadth first search test
+class: "PacmanSearchTest"
+algorithm: "breadthFirstSearch"
+
+# The following specifies the layout to be used 
+layoutName: "mediumMaze"
+layout: """
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                 P%
+% %%%%%%%%%%%%%%%%%%%%%%% %%%%%%%% %
+% %%   %   %      %%%%%%%   %%     %
+% %% % % % % %%%% %%%%%%%%% %% %%%%%
+% %% % % % %             %% %%     %
+% %% % % % % % %%%%  %%%    %%%%%% %
+% %  % % %   %    %% %%%%%%%%      %
+% %% % % %%%%%%%% %%        %% %%%%%
+% %% %   %%       %%%%%%%%% %%     %
+%    %%%%%% %%%%%%%      %% %%%%%% %
+%%%%%%      %       %%%% %% %      %
+%      %%%%%% %%%%% %    %% %% %%%%%
+% %%%%%%      %       %%%%% %%     %
+%        %%%%%% %%%%%%%%%%% %%  %% %
+%%%%%%%%%%                  %%%%%% %
+%.         %%%%%%%%%%%%%%%%        %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+"""
+

search/test_cases/q3/CONFIG

@@ -0,0 +1,2 @@
+class: "PassAllTestsQuestion"
+max_points: "3"

search/test_cases/q3/graph_backtrack.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q3/graph_backtrack.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "1:A->C 0:C->G"
+expanded_states: "A B C D"
+rev_solution: "1:A->C 0:C->G"
+rev_expanded_states: "A B C D"

search/test_cases/q3/graph_backtrack.test

@@ -0,0 +1,32 @@
+class: "GraphSearchTest"
+algorithm: "uniformCostSearch"
+
+diagram: """
+ B   
+ ^
+ |
+*A --> C --> G
+ |
+ V
+ D
+
+A is the start state, G is the goal.  Arrows mark 
+possible state transitions.  This tests whether
+you extract the sequence of actions correctly even
+if your search backtracks.  If you fail this, your
+nodes are not correctly tracking the sequences of
+actions required to reach them.
+"""
+# The following section specifies the search problem and the solution.
+# The graph is specified by first the set of start states, followed by
+# the set of goal states, and lastly by the state transitions which are
+# of the form: 
+#      <start state> <actions> <end state> <cost>
+graph: """
+start_state: A
+goal_states: G
+A 0:A->B B 1.0
+A 1:A->C C 2.0
+A 2:A->D D 4.0
+C 0:C->G G 8.0
+"""

search/test_cases/q3/graph_bfs_vs_dfs.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases/q3/graph_bfs_vs_dfs.test.
+# This solution is designed to support both right-to-left
+# and left-to-right implementations.
+solution: "1:A->G"
+expanded_states: "A B"
+rev_solution: "1:A->G"
+rev_expanded_states: "A B"