SearchProject/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 typing import List

import util
from game import Directions


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: SearchProblem) -> List[Directions]:
    """
    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.
    """
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s, s, w, s, w, w, s, w]

def depthFirstSearch(problem: SearchProblem) -> List[Directions]:
    """
    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 ***"
    st: util.Stack = util.Stack()
    vis: set = set()
    st.push((problem.getStartState(), []))  # current node, path

    while not st.isEmpty():
        currNode, paths = st.pop()
        if problem.isGoalState(currNode):
            return paths
        if currNode not in vis:
            vis.add(currNode)
            for nextNode, path, _ in problem.getSuccessors(currNode):
                if nextNode not in vis:
                    st.push((nextNode, paths + [path]))
    return []


def breadthFirstSearch(problem: SearchProblem) -> List[Directions]:
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    q: util.Queue = util.Queue()
    vis: set = set()
    q.push((problem.getStartState(), []))  # current node, path

    while not q.isEmpty():
        currNode, paths = q.pop()
        if problem.isGoalState(currNode):
            return paths
        if currNode not in vis:
            vis.add(currNode)
            for nextNode, path, _ in problem.getSuccessors(currNode):
                if nextNode not in vis:
                    q.push((nextNode, paths + [path]))
    return []


def uniformCostSearch(problem: SearchProblem) -> List[Directions]:
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    pq: util.PriorityQueue = util.PriorityQueue()
    vis: set = set()
    pq.push(
        item=(problem.getStartState(), [], 0), priority=0
    )  # item: state, paths, cost; priority: priority

    while not pq.isEmpty():
        currNode, paths, cost = pq.pop()
        if problem.isGoalState(currNode):
            return paths
        if currNode not in vis:
            vis.add(currNode)
            for nextNode, path, stepCost in problem.getSuccessors(currNode):
                if nextNode not in vis:
                    pq.push(
                        item=(nextNode, paths + [path], cost + stepCost),
                        priority=cost + stepCost,
                    )
    return []


def nullHeuristic(state, problem=None) -> float:
    """
    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: SearchProblem, heuristic=nullHeuristic) -> List[Directions]:
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"

    def priorityFunction(item):
        state, path, g = item
        return g + heuristic(state, problem)

    pqwf: util.PriorityQueueWithFunction = util.PriorityQueueWithFunction(
        priorityFunction
    )
    vis: set = set()
    pqwf.push((problem.getStartState(), [], 0))

    while not pqwf.isEmpty():
        currNode, paths, cost = pqwf.pop()
        if problem.isGoalState(currNode):
            return paths
        if currNode not in vis:
            vis.add(currNode)
            for nextNode, path, stepCost in problem.getSuccessors(currNode):
                if nextNode not in vis:
                    pqwf.push((nextNode, paths + [path], cost + stepCost))
    return []


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