# -*- coding: utf-8 -*- # 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). # # IFT-7025 Hier 2019 # François Pelletier # 908144032 """ 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 ***" maPile = util.Stack() maListeFermee = [] # Verifier si le depart est un but if problem.isGoalState(problem.getStartState()): return [] # Mettre le premier noeud et un chemin vide dans la pile maPile.push((problem.getStartState(),list())) # Rechercher tant que la pile n'est pas vide while (not maPile.isEmpty()): # Extraire le noeud courant noeudCourant, listeDirections = maPile.pop() # print "Noeud courant: ", noeudCourant # La marquer comme visite maListeFermee.append(noeudCourant) # Verifier si c'est un but if(problem.isGoalState(noeudCourant)): # print "Nous avons atteint le but !" return listeDirections # Effectuer la recherche de successeurs for s in problem.getSuccessors(noeudCourant): # Decomposer le tuple pour faciliter la lecture succNoeudCourant, succDirection, succCost = s # Verifier si le noeud n'est pas deja visite et n'est pas dans la pile if (succNoeudCourant not in maListeFermee): # Dans ce cas, ajouter le noeud et le chemin pour s'y rendre a la pile # print "Ajout du noeud a la pile: ", succNoeudCourant maPile.push((succNoeudCourant,listeDirections+[succDirection])) # print "pas de solution" util.raiseNotDefined() def breadthFirstSearch(problem): """Search the shallowest nodes in the search tree first.""" "*** YOUR CODE HERE ***" maFile = util.Queue() maListeFermee = [] # Verifier si le depart est un but if problem.isGoalState(problem.getStartState()): return [] # Mettre le premier noeud et un chemin vide dans la file maFile.push((problem.getStartState(),list())) while (not maFile.isEmpty()): # Extraire le noeud courant noeudCourant, listeDirections = maFile.pop() #print "Noeud courant: ", noeudCourant # La marquer comme visite maListeFermee.append(noeudCourant) # Verifier si c'est un but if(problem.isGoalState(noeudCourant)): #print "Nous avons atteint le but !" return listeDirections # Effectuer la recherche de successeurs for s in problem.getSuccessors(noeudCourant): # Decomposer le tuple pour faciliter la lecture succNoeudCourant, succDirection, succCost = s # Verifier si le noeud n'est pas deja visite et n'est pas dans la file if (succNoeudCourant not in maListeFermee+[i[0] for i in maFile.list]): # Dans ce cas, ajouter le noeud et le chemin pour s'y rendre a la file #print "Ajout du noeud a la file: ", succNoeudCourant maFile.push((succNoeudCourant,listeDirections+[succDirection])) # print "pas de solution" util.raiseNotDefined() def uniformCostSearch(problem): """Search the node of least total cost first.""" "*** YOUR CODE HERE ***" maFileEtat = util.PriorityQueue() # Je fais une 2e file parce que sinon je ne peux pas comparer le cout # si je le met dans le meme tuple que le chemin maFileChemin = util.PriorityQueue() maListeFermee = [] # Verifier si le depart est un but if problem.isGoalState(problem.getStartState()): return [] # Mettre le premier noeud dans la file maFileEtat.push(problem.getStartState(),0) # Extraire le noeud courant listeDirections = [] while (not maFileEtat.isEmpty()): noeudCourant = maFileEtat.pop() #print "Noeud courant: ", noeudCourant # La marquer comme visite if noeudCourant not in maListeFermee: maListeFermee.append(noeudCourant) # Verifier si c'est un but if(problem.isGoalState(noeudCourant)): #print "Nous avons atteint le but !" return listeDirections # Effectuer la recherche de successeurs for s in problem.getSuccessors(noeudCourant): # Decomposer le tuple pour faciliter la lecture succNoeudCourant, succDirection, succCost = s # Verifier si le noeud n'est pas deja visite succCostChemin = None if (succNoeudCourant not in maListeFermee+[i[0] for i in maFileEtat.heap]): # obtenir le cout total du chemin succCostChemin = problem.getCostOfActions(listeDirections+[succDirection]) # Si le noeud est encore dans la file, mettre a jour la priorite si elle devient inferieure #print "Ajout du noeud a la file: ", succNoeudCourant maFileEtat.push(succNoeudCourant,succCostChemin) # Ajouter le nouveau chemin dans la file avec son cout maFileChemin.push(listeDirections+[succDirection],succCostChemin) # si noeud existant, mettre a jour le cout si meilleur elif succNoeudCourant in [i[0] for i in maFileEtat.heap]: maFileEtat.update(succNoeudCourant,succCostChemin) listeDirections = maFileChemin.pop() # print "pas de solution" 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 ***" maFileEtat = util.PriorityQueue() # Je fais une 2e file parce que sinon je ne peux pas comparer le cout # si je le met dans le meme tuple que le chemin maFileChemin = util.PriorityQueue() maListeFermee = [] # Verifier si le depart est un but if problem.isGoalState(problem.getStartState()): return [] # Mettre le premier noeud dans la file maFileEtat.push(problem.getStartState(),0) # Extraire le noeud courant listeDirections = [] while (not maFileEtat.isEmpty()): noeudCourant = maFileEtat.pop() # print "Noeud courant: ", noeudCourant # La marquer comme visite if noeudCourant not in maListeFermee: maListeFermee.append(noeudCourant) # Verifier si c'est un but if(problem.isGoalState(noeudCourant)): #print "Nous avons atteint le but !" return listeDirections # Effectuer la recherche de successeurs for s in problem.getSuccessors(noeudCourant): # Decomposer le tuple pour faciliter la lecture succ_noeud_courant, succDirection, succCost = s # Verifier si le noeud n'est pas deja visite succ_cost_chemin = None if succ_noeud_courant not in maListeFermee+[i[0] for i in maFileEtat.heap]: # obtenir le cout total du chemin succ_cost_chemin = problem.getCostOfActions(listeDirections+[succDirection])+heuristic(succ_noeud_courant,problem) # Si le noeud est encore dans la file, mettre a jour la priorite si elle devient inferieure # print "Ajout du noeud a la file: ", succ_noeud_courant maFileEtat.push(succ_noeud_courant,succ_cost_chemin) # Ajouter le nouveau chemin dans la file avec son cout maFileChemin.push(listeDirections+[succDirection],succ_cost_chemin) # si noeud existant, mettre a jour le cout si meilleur elif succ_noeud_courant in [i[0] for i in maFileEtat.heap]: maFileEtat.update(succ_noeud_courant,succ_cost_chemin) listeDirections = maFileChemin.pop() # print "pas de solution" util.raiseNotDefined() # Abbreviations bfs = breadthFirstSearch dfs = depthFirstSearch astar = aStarSearch ucs = uniformCostSearch