# search.py # --------- # Licensing Information: Please do not distribute or publish solutions to this # project. You are free to use and extend these projects for educational # purposes. The Pacman AI projects were developed at UC Berkeley, primarily by # John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu). # For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html """ 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 [2nd Edition: p 75, 3rd Edition: p 87] Your search algorithm needs to return a list of actions that reaches the goal. Make sure to implement a graph search algorithm [2nd Edition: Fig. 3.18, 3rd Edition: Fig 3.7]. 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() def breadthFirstSearch(problem): """ Search the shallowest nodes in the search tree first. [2nd Edition: p 73, 3rd Edition: p 82] """ "*** YOUR CODE HERE ***" util.raiseNotDefined() def uniformCostSearch(problem): "Search the node of least total cost first. " "*** YOUR CODE HERE ***" 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 ***" util.raiseNotDefined() # Abbreviations bfs = breadthFirstSearch dfs = depthFirstSearch astar = aStarSearch ucs = uniformCostSearch