AI_learning/search/search.py

# -*- coding:utf-8 -*-  
# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created 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 (pabbeel@cs.berkeley.edu).


"""
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())
    """
    "*** YOUR CODE HERE ***"
    from util import Stack
    from game import Directions

    frontier=Stack()
    visited=[]
    frontier.push((problem.getStartState(),[]))

    while not frontier.isEmpty():
        cur_node,actions=frontier.pop()
        if problem.isGoalState(cur_node):
            return actions
        
        if cur_node not in visited:
            expand=problem.getSuccessors(cur_node)
            visited.append(cur_node)
            for location,direction,cost in expand:
                if location not in visited:
                    frontier.push((location,actions+[direction]))
    util.raiseNotDefined()

def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    frontier=util.Queue()
    visited=[]
    frontier.push((problem.getStartState(),[]))

    while not frontier.isEmpty():
        cur_node,actions=frontier.pop()
        if problem.isGoalState(cur_node):
            return actions
        
        if cur_node not in visited:
            expand=problem.getSuccessors(cur_node)
            visited.append(cur_node)
            for location,direction,cost in expand:
                if location not in visited:
                    frontier.push((location,actions+[direction]))
    util.raiseNotDefined()

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

    frontier = util.PriorityQueueWithFunction(lambda x: x[2])
    visited=[]
    frontier.push((problem.getStartState(),None,0))

    path = []                                                      
    parentSeq = {}
    parentSeq[(problem.getStartState(),None,0)]=None
    while not frontier.isEmpty():
        current_fullstate=frontier.pop()
        # print(current_fullstate)
        cur_node=current_fullstate[0]
        actions=current_fullstate[1]
        if problem.isGoalState(cur_node):
            break
        
        if cur_node not in visited:
            expand=problem.getSuccessors(cur_node)
            visited.append(cur_node)
            for state in expand:
                location = state[0]
                direction = state[1]
                cost=current_fullstate[2]+state[2]
                if location not in visited:
                    frontier.push((location,direction,cost))
                    parentSeq[(location,direction)] = current_fullstate
                # elif location in visited:
                #     frontier.update((location,direction,cost))
    child = current_fullstate
 
    while (child != None):
        path.append(child[1])
        if child[0] != problem.getStartState():
            child = parentSeq[(child[0],child[1])]
        else:
            child = None
    path.reverse()
    return path[1:]

    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):
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"
    from sets import Set
    frontier = util.PriorityQueue()                       
    actions = []                                    
    frontier.push((problem.getStartState(),actions),0)   
    visited = []                               

    while frontier:
        cur_node,actions = frontier.pop()      
        if problem.isGoalState(cur_node):
            return actions  
        if cur_node not in visited:
            visited.append(cur_node)
            expand = problem.getSuccessors(cur_node)
            for successor, action, cost in expand:
                tempActions = actions + [action]
                nextCost = problem.getCostOfActions(tempActions) + heuristic(successor,problem)     
                if successor not in visited:
                    frontier.push((successor,tempActions),nextCost)

    util.raiseNotDefined()


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