search.py

# search.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

"""
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
  [2nd Edition: p 75, 3rd Edition: p 87]
  
  Your search algorithm needs to return a list of actions that reaches
  the goal.  Make sure to implement a graph search algorithm 
  [2nd Edition: Fig. 3.18, 3rd Edition: Fig 3.7].
  
  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())
  """

  start = problem.getStartState()
  startPath = initPath(start)

  # step 1: initialize the frontier using the initial state of the problem
  frontier = util.Stack()
  frontierSet = set()

  frontier.push(startPath)
  frontierSet.add(start)

  # step 2: initialize the explored set to be empty
  explored = set()

  while True:
    # step 3: if the frontier is empty, return failure
    if frontier.isEmpty():
      return False

    # step 4: choose a leaf node and remove it from the frontier
    path = frontier.pop()
    target  = pathTarget(path)
    actions = pathActions(path)
    frontierSet.remove(target)

    # step 5: if the node contans a goal state, return the corresponding solution
    if problem.isGoalState(target):
      return actions

    # step 6: add the node to the explored set
    explored.add(target)

    # step 7: expand the node
    successors = problem.getSuccessors(target)

    # step 8: add the resulting nodes to the frontier, if not in frontier or explored set
    for nextPath in map(makePath, successors):
      nextTarget = pathTarget(nextPath)
      if not nextTarget in explored and not nextTarget in frontierSet:
        nextActions = actions + pathActions(nextPath)
        nextCost = pathCost(nextPath)
        nextTuple = (nextTarget, nextActions, nextCost)

        frontier.push(nextTuple)
        frontierSet.add(nextTarget)

def breadthFirstSearch(problem):
  """
  Search the shallowest nodes in the search tree first.
  [2nd Edition: p 73, 3rd Edition: p 82]
  """

  start = problem.getStartState()
  startPath = initPath(start)

  # step 1: initialize the frontier using the initial state of the problem
  frontier = util.Queue()
  frontierSet = set()

  frontier.push(startPath)
  frontierSet.add(start)

  # step 2: initialize the explored set to be empty
  explored = set()

  while True:
    # step 3: if the frontier is empty, return failure
    if frontier.isEmpty():
      return False

    # step 4: choose a leaf node and remove it from the frontier
    path = frontier.pop()
    target  = pathTarget(path)
    actions = pathActions(path)
    frontierSet.remove(target)

    # step 5: if the node contans a goal state, return the corresponding solution
    if problem.isGoalState(target):
      return actions

    # step 6: add the node to the explored set
    explored.add(target)

    # step 7: expand the node
    successors = problem.getSuccessors(target)

    # step 8: add the resulting nodes to the frontier, if not in frontier or explored set
    for nextPath in map(makePath, successors):
      nextTarget = pathTarget(nextPath)
      if not nextTarget in explored and not nextTarget in frontierSet:
        nextActions = actions + pathActions(nextPath)
        nextCost = pathCost(nextPath)
        nextTuple = (nextTarget, nextActions, nextCost)

        frontier.push(nextTuple)
        frontierSet.add(nextTarget)

def uniformCostSearch(problem):
  "Search the node of least total cost first. "

  start = problem.getStartState()
  startPath = initPath(start)

  # step 1: initialize the frontier using the initial state of the problem
  frontier = util.PriorityQueue()
  frontierSet = set()

  frontier.push(startPath, 0)
  frontierSet.add(start)

  # step 2: initialize the explored set to be empty
  explored = set()

  while True:
    # step 3: if the frontier is empty, return failure
    if frontier.isEmpty():
      return False

    # step 4: choose a leaf node and remove it from the frontier
    path = frontier.pop()
    target  = pathTarget(path)
    actions = pathActions(path)
    frontierSet.remove(target)

    # step 5: if the node contans a goal state, return the corresponding solution
    if problem.isGoalState(target):
      return actions

    # step 6: add the node to the explored set
    explored.add(target)

    # step 7: expand the node
    successors = problem.getSuccessors(target)

    # step 8: add the resulting nodes to the frontier, if not in frontier or explored set
    for nextPath in map(makePath, successors):
      nextTarget = pathTarget(nextPath)
      if not nextTarget in explored and not nextTarget in frontierSet:
        nextActions = actions + pathActions(nextPath)
        nextPriority = problem.getCostOfActions(nextActions)
        nextCost = pathCost(nextPath)
        nextTuple = (nextTarget, nextActions, nextCost)

        frontier.push(nextTuple, nextPriority)
        frontierSet.add(nextTarget)

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."

  start = problem.getStartState()
  startPath = initPath(start)

  # step 1: initialize the frontier using the initial state of the problem
  frontier = util.PriorityQueue()
  frontierSet = set()

  frontier.push(startPath, 0)
  frontierSet.add(start)

  # step 2: initialize the explored set to be empty
  explored = set()

  while True:
    # step 3: if the frontier is empty, return failure
    if frontier.isEmpty():
      return False

    # step 4: choose a leaf node and remove it from the frontier
    path = frontier.pop()
    target  = pathTarget(path)
    actions = pathActions(path)
    frontierSet.remove(target)

    # step 5: if the node contans a goal state, return the corresponding solution
    if problem.isGoalState(target):
      return actions

    # step 6: add the node to the explored set
    explored.add(target)

    # step 7: expand the node
    successors = problem.getSuccessors(target)

    # step 8: add the resulting nodes to the frontier, if not in frontier or explored set
    for nextPath in map(makePath, successors):
      nextTarget = pathTarget(nextPath)
      if not nextTarget in explored and not nextTarget in frontierSet:
        nextActions = actions + pathActions(nextPath)
        nextPriority = problem.getCostOfActions(nextActions) + heuristic(nextTarget, problem)
        nextCost = pathCost(nextPath)
        nextTuple = (nextTarget, nextActions, nextCost)

        frontier.push(nextTuple, nextPriority)
        frontierSet.add(nextTarget)
    
  
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch

# Private helpers

def pathTarget(path):
  return path[0]

def pathActions(path):
  return path[1]

def pathCost(path):
  return path[2]

def initPath(state):
  return (state, [], 0)

def makePath(successor):
  return (successor[0], [ successor[1] ], successor[2])