searchAgents.py

# 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).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/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 searchFunction=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
import searchAgents

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 dir(searchAgents):
        heur = getattr(searchAgents, 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 dir(searchAgents) or not prob.endswith('Problem'):
      raise AttributeError, prob + ' is not a search problem type in SearchAgents.py.'
    self.searchType = getattr(searchAgents, 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):
    """
    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
    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:
       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))
    self.heuristicInfo = {}

    #boolean tuple keeps track of which corners have been visited
    self.corner_bool = (False, False, False, False)

    #this problem only needs to know pacman's postion and if the corners have been visited
    #so we treat a tuple of those elements as the state
    self.startState = (self.startingPosition, self.corner_bool)
    #else:

    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

    self._visited, self.visitedlist = {}, []

  def getStartState(self):
    "Returns the start state (in your state space, not the full Pacman state space)"
    return self.startState

  def isGoalState(self, state):
    """
    Returns whether this search state is a goal state of the problem
    """
    """f state[0] not in self.visitedlist:
        self._visited[state[0]] = True
        self.visitedlist.append(state[0])
    """
    #if pacman has been to all the corners, the goal is reached. This is when the corner_bool
    #encoded in state[1] has all 4 elements reading true 
    return state[1].count(True) == 4

  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 = state[0]
      dx, dy = Actions.directionToVector(action)
      nextx, nexty = int(x + dx), int(y + dy)
      hitsWall = self.walls[nextx][nexty]
      if not hitsWall:
          if (nextx, nexty) in self.corners:
              idx = self.corners.index((nextx, nexty))
              new_bool = list(state[1])
              new_bool[idx] = True
              new_bool = tuple(new_bool)
          else: new_bool = state[1]


          nextState = ((nextx, nexty), new_bool)
          successors.append((nextState, action, 1))

    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.  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 manhattanDistance(pos1, pos2):
    """
    returns a general manhattan distance between any 2 points

    pos1: position coordinated in n-space (tuple)
    pos2: position coordinated in n-space (tuple)
    """
    if len(pos1) != len(pos2):
        raise Exception('Coordiantes must be of the same dimension')

    return abs(pos1[0] - pos2[0]) + abs(pos1[1] - pos2[1])

def euclidianDistance(pos1, pos2):
    """
    returns a general manhattan distance between any 2 points

    pos1: position coordinated in n-space (tuple)
    pos2: position coordinated in n-space (tuple)
    """
    euc_dist = 0
    if len(pos1) != len(pos2):
        raise Exception('Coordiantes must be of the same dimension')
    for n in xrange(len(pos1)):
        euc_dist += (pos1[n] - pos2[n]) ** 2
    return euc_dist**.5

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.  (You need not worry about consistency for
  this heuristic to receive full credit.)
  """

  corners = problem.corners # These are the corner coordinates
  walls = problem.walls # These are the walls of the maze, as a Grid (game.py)

  corner_dist = []

  new_corners = list(corners)
  for c in state[1]:
      if c == True:
          del new_corners[state[1].index(c)]

  if len(new_corners) == 0: return 0
  for i in xrange(len(new_corners)):
      corner_dist.append(manhattanDistance(state[0], new_corners[i]))

  close_corner = min(corner_dist)
  start_idx = corner_dist.index(close_corner)

  man_dist = manhattanDistance(state[0],new_corners[start_idx])
  used_corners = [False, False, False, False]
  used_corners[start_idx] = True

  for i in xrange(len(new_corners)):

      if used_corners[i] == True: continue
      if i == len(new_corners)-1:
          if used_corners[0]==True:
              man_dist +=  manhattanDistance(new_corners[i], new_corners[i-1])
          elif used_corners[i-1] == True:
              man_dist += manhattanDistance(new_corners[i], new_corners[0])
          else:
              man_dist += min(manhattanDistance(new_corners[i], new_corners[0]), manhattanDistance(new_corners[i], new_corners[i-1]))
          used_corners[i] = True
      else:
          if used_corners[i-1]==True:
              man_dist +=  manhattanDistance(new_corners[i], new_corners[i+1])
          elif used_corners[i+1] == True:
              man_dist += manhattanDistance(new_corners[i], new_corners[i+1])
          else:
              man_dist += min(manhattanDistance(new_corners[i], new_corners[i+1]), manhattanDistance(new_corners[i], new_corners[i-1]))
          used_corners[i] = True

  return man_dist



  """
  man_dist = 0
  for n in range(len(corners)):
  	if state[1][n] == False:
   		cur_dist = manhattanDistance(state[0], corners[n])
  		if cur_dist > man_dist:
			man_dist = cur_dist
  return man_dist
  """

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
  food_list = foodGrid.asList()

  #create and fill list of manhattan distances between pac man and all food items
  food_dist = []
  for food in food_list:
        food_dist.append(manhattanDistance(position, food))

  #while there is food on the board return the maximum distance between
  #pacman and a food item, otherwise return 0 since goal state
  if len(food_list) != 0:

        min_food = min(food_dist)
        min_idx = food_dist.index(min_food)

        max_food = max(food_dist)
        max_idx = food_dist.index(max_food)

        min_max = manhattanDistance(food_list[min_idx], food_list[max_idx])
        #print min_max + min(food_dist)
        return max_food
  #return 0 when goal state
  else: 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)

    return search.uniformCostSearch(problem)

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):
    """
    self.food is a Grid object, so to find the closest food test if your state is a food object
    """
    x,y = state
    #if the position pacman is in has a food item, the closest food has been found
    #so this is a goal state
    return self.food[x][y]

##################
# 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: ' + str(point1)
  assert not walls[x2][y2], 'point2 is a wall: ' + str(point2)
  prob = PositionSearchProblem(gameState, start=point1, goal=point2, warn=False)
  return len(search.bfs(prob))