intro2ai/p1_search/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).
"""

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 genericSearch(problem, getNewCostAndPriority):
    fringe = util.PriorityQueue()
    startState = problem.getStartState()
    fringe.push((startState, [], 0), 0)
    visited = {}

    while True:
        if fringe.isEmpty():
            raise Exception("No path found.")

        state, actions, cost = fringe.pop()

        if problem.isGoalState(state):
            return actions

        if state in visited and cost >= visited[state]:
            continue
        visited[state] = cost

        for successor, action, stepCost in problem.getSuccessors(state):
            newCost, priority = getNewCostAndPriority(cost, stepCost, successor)
            newActions = list(actions) + [action]
            fringe.push((successor, newActions, newCost), priority)


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.
    """
    def getNewCostAndPriority(cost, stepCost, successor):
        newCost = cost + 1
        return newCost, -newCost
    return genericSearch(problem, getNewCostAndPriority)


def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""
    def getNewCostAndPriority(cost, stepCost, successor):
        newCost = cost + 1
        return newCost, newCost
    return genericSearch(problem, getNewCostAndPriority)


def uniformCostSearch(problem):
    """Search the node of least total cost first."""
    def getNewCostAndPriority(cost, stepCost, successor):
        newCost = cost + stepCost
        return newCost, newCost
    return genericSearch(problem, getNewCostAndPriority)


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 ***"
    def getNewCostAndPriority(cost, stepCost, successor):
        newCost = cost + stepCost
        newPriority = newCost + heuristic(successor, problem)
        return newCost, newPriority
    return genericSearch(problem, getNewCostAndPriority)


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