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).
# 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

"""
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())
    """
    from util import Stack
    from game import Directions
    
    actions = []
    
    frontier = Stack()
    frontier.push((problem.getStartState(), [], 0))
    visited = []

    while (frontier.isEmpty() == False):
        currentS, currentP, currentC = frontier.pop()
        if (problem.isGoalState(currentS) == True):
            actions = currentP
            break
        if (visited.count(currentS) == 0):
            visited.append(currentS)
        successors = problem.getSuccessors(currentS)
        for i in range(0,len(successors)):
            (neighbor, direction, cost) = successors[i]
            if (visited.count(neighbor) == 0):
                frontier.push((neighbor, (currentP +[direction]), (currentC + cost)))
                
    return actions
             
    util.raiseNotDefined()

def breadthFirstSearch(problem):
    """
    Search the shallowest nodes in the search tree first.
    """

    from util import Queue
    from game import Directions
    
    actions = []
    
    frontier = Queue()
    frontier.push((problem.getStartState(), [], 0))
    visited = []

    while (frontier.isEmpty() == False):
        (currentS, currentP, currentC) = frontier.pop()
        if (problem.isGoalState(currentS) == True):
            actions = currentP
            break
        if (visited.count(currentS) == 0):
            visited.append(currentS)
            successors = problem.getSuccessors(currentS)
            for i in range(0,len(successors)):
                (neighbor, direction, cost) = successors[i]
                if (visited.count(neighbor) == 0):
                    frontier.push((neighbor, (currentP +[direction]), (currentC + cost)))
    print actions
    return actions

    util.raiseNotDefined()

def uniformCostSearch(problem):


    from util import PriorityQueue
    from game import Directions
    
    actions = []
    
    frontier = PriorityQueue()
    frontier.push((problem.getStartState(), [], 0), 0)
    visited = []

    while (frontier.isEmpty() == False):
        (currentS, currentP, currentC) = frontier.pop()
        if (problem.isGoalState(currentS) == True):
            actions = currentP
            break
        if (visited.count(currentS) == 0):
            visited.append(currentS)
            successors = problem.getSuccessors(currentS)
            for i in range(0,len(successors)):
                (neighbor, direction, cost) = successors[i]
                if (visited.count(neighbor) == 0):
                    frontier.push((neighbor, (currentP +[direction]), (currentC + cost)), (currentC + cost))
                
    return actions

    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):

    from util import PriorityQueue
    from game import Directions
    
    actions = []
    
    frontier = PriorityQueue()
    frontier.push((problem.getStartState(), [], 0), 0)
    visited = []

    while (frontier.isEmpty() == False):
        (currentS, currentP, currentC) = frontier.pop()
        if (problem.isGoalState(currentS) == True):
            actions = currentP
            break
        if (visited.count(currentS) == 0):
            visited.append(currentS)
            successors = problem.getSuccessors(currentS)
            for i in range(0,len(successors)):
                (neighbor, direction, cost) = successors[i]
                if (visited.count(neighbor) == 0):
                    frontier.push((neighbor, (currentP +[direction]), (currentC + cost)), (currentC + cost + heuristic(neighbor, problem)))
                
    return actions


    util.raiseNotDefined()


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch