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# 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 ***" # util.raiseNotDefined() fringe = util.Stack() visited = [] fringe.push((problem.getStartState(), [])) while not fringe.isEmpty(): currState, actionList = fringe.pop() if problem.isGoalState(currState): return actionList if currState not in visited: for nextState, action, cost in problem.getSuccessors(currState): fringe.push((nextState, actionList + [action])) visited.append(currState) return None def breadthFirstSearch(problem): """Search the shallowest nodes in the search tree first.""" "*** YOUR CODE HERE ***" # util.raiseNotDefined() fringe = util.Queue() visited = [] fringe.push((problem.getStartState(), [])) while not fringe.isEmpty(): currState, actionList = fringe.pop() if problem.isGoalState(currState): return actionList if currState not in visited: for nextState, action, cost in problem.getSuccessors(currState): fringe.push((nextState, actionList + [action])) visited.append(currState) return None def uniformCostSearch(problem): """Search the node of least total cost first.""" "*** YOUR CODE HERE ***" # util.raiseNotDefined() fringe = util.PriorityQueue() visited = [] fringe.push((problem.getStartState(), [], 0), 0) while not fringe.isEmpty(): currState, actionList, currCost = fringe.pop() if problem.isGoalState(currState): return actionList if currState not in visited: for nextState, action, cost in problem.getSuccessors(currState): fringe.push((nextState, actionList + [action], currCost + cost), currCost + cost) visited.append(currState) return None 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 ***" util.raiseNotDefined() # Abbreviations bfs = breadthFirstSearch dfs = depthFirstSearch astar = aStarSearch ucs = uniformCostSearch