searchAgents.py

# searchAgents.py
# ---------------


"""
This file contains all of the agents that can be selected to control Pacman.
"""

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

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

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
        "*** YOUR CODE HERE ***"

    def getStartState(self):
        """
        Returns the start state (in your state space, not the full Pacman state
        space)
        """
        # state here is defined as a tuple of state position and tuple of visited corners.
        # state = (state_location, visited_corners)
        # state[0] => state_locations
        # state[1] => visited_corners
        return (self.startingPosition, ())

    def isGoalState(self, state):
        """
        Returns whether this search state is a goal state of the problem.
        """
        visited_corners = state[1]
        return len(visited_corners) == len(self.corners)


    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]:
            location = state[0]
            x = location[0]
            y = location[1]
            goals_visited = state[1]
            dx, dy = Actions.directionToVector(action)
            nextx, nexty = int(x + dx), int(y + dy)
            hitsWall = self.walls[nextx][nexty]
            if not hitsWall:
                successor_location = (nextx, nexty)
                if successor_location in self.corners:
                    if successor_location not in goals_visited:
                        goals_visited = list(goals_visited)
                        goals_visited.append(successor_location)
                        goals_visited = tuple(goals_visited)
                successor_state = (successor_location, goals_visited)
                successor = (successor_state, action, 1)
                successors.append(successor)
        self._expanded += 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.
        """
        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.
    """
    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 ***"
    state_location = state[0]
    corners_visited = list(state[1])
    corners_not_visited = [x for x in corners if x not in corners_visited]
    if problem.isGoalState(state):
        return 0
    else:
        return aggregate_distance(state_location, corners_not_visited)

##Signature:
# manhat_dist_bw_two_pts : position_a position_b -> distance

##Purpose:
###GIVEN: two positions
###RETURNS: the manhattan distance(no diagonals) between the two positions.
def manhat_dist_bw_two_pts(xy1,xy2):
    return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])

##Signature:
# nearest_distance : list_of_corners point_location
#                                       -> (nearest_corner, distance_to_corner)

##Purpose:
###GIVEN: a list of corners and a position
###RETURNS: a tuple of the nearest corner to the given position and the distance
###         to the nearest corner.
def nearest_distance(corners, state_loc):
    list_of_dist = []
    for x in corners:
        dist = manhat_dist_bw_two_pts(x, state_loc)
        list_of_dist.append((x, dist))
    list_of_dist.sort(key = lambda x : x[1])
    nearest_corner = list_of_dist[0][0]
    dist_to_corner = list_of_dist[0][1]
    return (nearest_corner, dist_to_corner)

##Signature:
# start_location goals_not_visited -> distance

##Purpose:
###GIVEN: the start positon and the list of unvisited goals
###RETURNS: the aggregated manhattan distance(no diagonals) between the continually closest
###         points in the list, starting from the given start position.
def aggregate_distance(state_location, goals_not_visited):
    distance = 0
    nearest_goal = state_location
    while len(goals_not_visited) >= 1:
        nearest_goal, dist_to_goal = nearest_distance(goals_not_visited, nearest_goal)
        goals_not_visited.remove(nearest_goal)
        distance += dist_to_goal
    return distance


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 = {}

    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):
    """
    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.
    """
    position, foodGrid = state
    walls = problem.walls
    
    goals_list = foodGrid.asList()
    heuristic_val = heuristic_5(position, goals_list, walls)
    return heuristic_val

## Wierd name, i know, but i'm calling this cause this was my 5th heuristic.
## Lets say, its an artifact of my varied experimentations :)
def heuristic_5(position, goals_list, walls):
    if len(goals_list) == 0:
        return 0
    elif len(goals_list) == 1:
        return manhat_dist_bw_two_pts(position, goals_list[0])
    else:
        furthest_goal_from_pos = furthest_dist_goal_w(goals_list, position, walls)
        furthest_from_furthest_goal = furthest_dist_goal_w(goals_list, furthest_goal_from_pos, walls)
        dist_bw_furthest_goals = maze_distance(furthest_goal_from_pos, furthest_from_furthest_goal, walls)
        closer_goal, dist_to_closer_goal = nearest_goal_to_posn_w(goals_list, position, walls)
        final_heuristic_distance = dist_bw_furthest_goals + dist_to_closer_goal
        return final_heuristic_distance

def nearest_goal_to_posn_w(goal_list, position, walls):
    list_of_dist = []
    for goal in goal_list:
        dist = maze_distance(goal, position, walls)
        list_of_dist.append((goal, dist))
    list_of_dist.sort(key = lambda x : x[1])
    nearest_goal = list_of_dist[0][0]
    dist_to_nearest_goal = list_of_dist[0][1]
    return (nearest_goal, dist_to_nearest_goal)

def furthest_dist_goal_w(goal_list, position, walls):
    list_of_dist = []
    for goal in goal_list:
        dist = maze_distance(goal, position, walls)
        list_of_dist.append((goal, dist))
    list_of_dist.sort(key = lambda x : x[1], reverse = True)
    furthest_goal = list_of_dist[0][0]
    dist_to_goal = list_of_dist[0][1]
    return furthest_goal

def maze_distance(point1, point2, walls):
    x1, y1 = point1
    x2, y2 = point2
    from pacman import GameState
    new_game_state = TestGameState(walls, point1)
    prob = PositionSearchProblem(new_game_state, start=point1, goal=point2, warn=False, visualize=False)
    return len(search.bfs(prob))

from pacman import GameState
class TestGameState(GameState):
    def __init__(self, walls, start, food=None):
        self.walls = walls
        if food != None: self.food = food
        self.num_food = 1
        self.pacman_position = start
    def getWalls(self):
        return self.walls
    def getPacmanPosition(self):
        return self.pacman_position
    def getNumFood(self):
        return 1
    def hasFood(self, x, y):
        return False

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)

        from search import bfs
        return bfs(problem)

class AnyFoodSearchProblem(PositionSearchProblem):
    """
    A search problem for finding a path to any food.
    """

    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

        return self.food[x][y]

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))