aiproj1questions

Below someone named pyTony (who was also in my Python class last semester from MIT) posted the following solutions
to his project1, which I used to try to get mine working after the due date, since he had posted them, and then
they took down the solutions, perhaps since they want to reuse these questions another time, not sure.
Those are from search.py and all work (dfs, bfs, ucs, astar searching).  I don't know if you were in the MIT class
in Python last semester, but they were much more lenient (and may have changed since then) about not only posting
an official solution after the due date, but they also allowed ALL students who may have had different approaches
to the problem to post their solutions for comparison and learning purposes on the class Wiki, and in the cases where 
I got the answer wrong, I was able to correct it and move onto the next project the next week, or in the cases where 
I got it correct but someone had implemented a better and more efficient solution, I could also learn from that.

This semester all my programming courses come from various universities on the west coast, and perhaps even MIT on
the east coast is like this now, don't know, but they are very strict about NOT ever posting any solution of any
kind, and you are left hanging if you don't get it, and although I do understand that you learn better if you do not
ever get help, well, in this class, I won't be doing project3 unless I get help, so there will be no learning there
for me.  In fact the only class I'm taking that is from the east coast this semester is "Justice", an interestng
diversion from all the tech stuff I usually take! ;-)  Since 2013 started though, took the following from the west
coast: databases, AI (Python), SaaS(Ruby/Rails), and next week starts the hardware/software interface (C - which I 
did for 18 years so a review!)  I also started the graphics class (OpenGL and C++) which I took 17 years ago so also a review, but found my software incompatible with their software packages for graphics, so could not go beyond the first project.  I also last semester completed another review class in networks, but that had no coding.  The common theme
though that I've found this semester in ALL the programming classes from the west coast is that they are very strict
about never posting ANY coding solutions of any kind, and then Sam Joseph told me about posting code for discussion
here with someone who wanted to chat here on a gist.

A little explanation:
When on the east coast I was not getting any job offers when I lost my job in the area in which I worked for 18 years,
embedded systems C coding, I started to review and expand, which is how I ended up in all these other languages of
programming courses.  My degrees are in EE not CS as well, so I may not have some of the foundations that others have!
So my apologies for stupid questions. ;-)

I guess they are giving us free online courses and have to autograde thousands and they also want to reuse their code
for the next semester, and if solutions are posted, then they cannot use them again, and perhaps in that case, they
would not have enough time to offer the course to more people by having to come up with more examples, don't know.

I know that for me though that in my case, if I don't get help with project1's issues or project2's issues, well, I
will just have to skip project3, and I understand, as mentioned above.

So I was able to correct my errors with searching from the answers below, but what PyTony did not give, was
a solution to the corners problem and the searches that came after that (will show below) if you have any
advice about those.  Those questions you will see below are in searchAgents.py.

The specific classes of methods that gave me trouble in project1 were: class CornersProblem (getStartState(),
isGoalState(), getSuccessors(), getCostOfActions()) and you will see I attempted this but did not get there with it.

I never got to: cornersHeuristic(), foodHeuristic(), and findPathtoClosestDot() or in the class AnyFoodSearchProblem
the method isGoalState().

Any help with these later parts of this project 1 from the AI class would be great! ;-)


# searchAgents.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).
# Student side autograding was added by Brad Miller, Nick Hay, and Pieter 
# Abbeel in Spring 2013.
# For more info, see http://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html

"""
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)
        """
        self.gameState = state
        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
        print "self.actions"
        print self.actions
        totalCost = problem.getCostOfActions(self.actions)
        print "total cost"
        print totalCost
        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 self.actions and (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

    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
        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)

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 # Number of search nodes expanded
        # Please add any code here which you would like to use
        # in initializing the problem
        "*** YOUR CODE HERE ***"
        self.startState = self.startingPosition
        self.goal = self.corners
        self.cornersVisited = 0
        self.cornerGoalsReached = []
        self.costFn = lambda pos: .5 ** pos[0]
        self.searchFunction = lambda prob: search.breadthFirstSearch(prob)
        #self.searchType = CornersProblem
        #self.searchFunction = search.breadthFirstSearch
        self.searchType = lambda state: CornersProblem(startingGameState)
        self.visualize = True
        # For display purposes
        self._visited, self._visitedlist, self._expanded = {}, [], 0
        
    def getStartState(self):
        "Returns the start state (in your state space, not the full Pacman state space)"
        "*** YOUR CODE HERE ***"
        return self.startState
        #util.raiseNotDefined()

    def isGoalState(self, state):
        "Returns whether this search state is a goal state of the problem"
        "*** YOUR CODE HERE ***"
        print state
        isGoal = False
        for goalState in self.goal:
            if state == goalState:
                if goalState not in self.cornerGoalsReached:
                    self.cornerGoalsReached.append(goalState)
                    self.cornersVisited += 1                        
                else:
                    if self.cornersVisited == 4:
                        isGoal = True

        # 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
        #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
            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
        if state not in self._visited:
            self._visited[state] = True
            self._visitedlist.append(state)
        print "successors"
        print successors
        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 ***"
    return 0 # 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
        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
        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 ***"
    return 0

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 ***"
        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

    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 ***"
        util.raiseNotDefined()

##################
# Mini-contest 1 #
##################

class ApproximateSearchAgent(Agent):
    "Implement your contest entry here.  Change anything but the class name."

    def registerInitialState(self, state):
        "This method is called before any moves are made."
        "*** YOUR CODE HERE ***"

    def getAction(self, state):
        """
        From game.py:
        The Agent will receive a GameState and must return an action from
        Directions.{North, South, East, West, Stop}
        """
        "*** YOUR CODE HERE ***"
        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: ' + 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.py
#My searches now that deadline is passed
#
#pyTony about 4 hours ago

#Here is my searches based on the generic search base.

def genericSearch(problem, fringe, h=nullHeuristic):
    """
    Generic Search in search tree

    Your search algorithm needs to return a list of actions that reaches
    the goal.  Make sure to implement a graph search algorithm
    """
    start = problem.getStartState()
    closed = set()
    fringe.push(((0, 0, start), []))
    while not fringe.isEmpty():
        (costHere, _, state), directions = fringe.pop()
        if problem.isGoalState(state):
            return directions
        if state not in closed:
            closed.add(state)
            for node, direction, cost in problem.getSuccessors(state):
                fringe.push(((costHere + cost, h(node, problem), node),
                            directions + [direction]))


def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first
    """
    return genericSearch(problem, util.Stack())

def breadthFirstSearch(problem):
    """
    Search the shallowest nodes in the search tree first.
    """
    return genericSearch(problem, util.Queue())

def cost(((c, h, n), direction)):
    return c + h

def uniformCostSearch(problem):
    """
    Search the node of least total cost first.
    """
    return genericSearch(problem, util.PriorityQueueWithFunction(cost))

def aStarSearch(problem, heuristic=nullHeuristic):
    """
    Search the node that has the lowest combined cost and heuristic first.
    """
    return genericSearch(problem, util.PriorityQueueWithFunction(cost), heuristic)