search.py

# 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).
"""

from re import S
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):
    from util import Stack
    """
    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:
    """
    
    closed = [problem.getStartState()] #Starting node is in the closed list
    fringe = Stack()

    for s in problem.getSuccessors(problem.getStartState()):
        fringe.push((s, [])) #Stores the successor and list of actions to get to that node

    while not fringe.isEmpty():
        node = fringe.pop()
        state = node[0]
        actions = node[1]
        #Adds the direction taken to get here to the list of actions
        actions.append(state[1])

        if problem.isGoalState(state[0]):
            return actions

        if state[0] not in closed:
            closed.append(state[0]) #Adds the coordinates to the closed list
            for child in problem.getSuccessors(state[0]):
                if child not in closed:
                    fringe.push((child, actions.copy())) #Adds child and a copy of actions to get here

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

    closed = [problem.getStartState()] #Starting node is in the closed list
    fringe = Queue()

    if problem.isGoalState(problem.getStartState()):
        return []

    for s in problem.getSuccessors(problem.getStartState()):
        fringe.push((s, [])) #Stores the successor and list of actions to get to that node

    while not fringe.isEmpty():
        node = fringe.pop()
        state = node[0]
        actions = node[1]
        #Adds the direction taken to get here to the list of actions
        actions.append(state[1])

        if problem.isGoalState(state[0]):
            return actions

        if state[0] not in closed:
            closed.append(state[0]) #Adds the coordinates to the closed list
            for child in problem.getSuccessors(state[0]):
                if child not in closed:
                    fringe.push((child, actions.copy())) #Adds child and a copy of actions to get here

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

    closed = [problem.getStartState()] #Starting node is in the closed list
    fringe = PriorityQueue()

    for s in problem.getSuccessors(problem.getStartState()):
        fringe.push((s, []), s[2]) #Stores the successor and list of actions to get there

    while not fringe.isEmpty():
        node = fringe.pop()
        state = node[0]
        actions = node[1]
        #Adds the direction taken to get here to the list of actions
        actions.append(state[1])

        if problem.isGoalState(state[0]):
            return actions

        if state[0] not in closed:
            closed.append(state[0]) #Adds the coordinates to the closed list
            for child in problem.getSuccessors(state[0]):
                if child not in closed:
                    #Update total cost of node
                    temp = list(child)
                    temp[2] += state[2]
                    fringe.push((temp, actions.copy()), temp[2]) #Adds child and a copy of actions to get here with priority of cost

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
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"
    closed = [problem.getStartState()] #Starting node is in the closed list
    fringe = PriorityQueue()

    for s in problem.getSuccessors(problem.getStartState()):
        fringe.push((s, []), s[2] + heuristic(s[0], problem)) #Stores the successor and list of actions to get there, priority of cost + heuristic

    while not fringe.isEmpty():
        node = fringe.pop()
        state = node[0]
        actions = node[1]
        #Adds the direction taken to get here to the list of actions
        actions.append(state[1])

        if problem.isGoalState(state[0]):
            return actions

        if state[0] not in closed:
            closed.append(state[0]) #Adds the coordinates to the closed list
            for child in problem.getSuccessors(state[0]):
                if child not in closed:
                    #Update total cost of node
                    temp = list(child)
                    temp[2] += state[2]
                    fringe.push((temp, actions.copy()), temp[2] + heuristic(temp[0], problem)) #Adds child and a copy of actions to get here with priority of cost + heuristic


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