263 lines
11 KiB
Python
263 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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# search.py
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# ---------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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#
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# IFT-7025 Hier 2019
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# François Pelletier
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# 908144032
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"""
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In search.py, you will implement generic search algorithms which are called by
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Pacman agents (in searchAgents.py).
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"""
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import util
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class SearchProblem:
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"""
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This class outlines the structure of a search problem, but doesn't implement
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any of the methods (in object-oriented terminology: an abstract class).
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You do not need to change anything in this class, ever.
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"""
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def getStartState(self):
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"""
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Returns the start state for the search problem.
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"""
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util.raiseNotDefined()
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def isGoalState(self, state):
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"""
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state: Search state
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Returns True if and only if the state is a valid goal state.
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"""
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util.raiseNotDefined()
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def getSuccessors(self, state):
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"""
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state: Search state
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For a given state, this should return a list of triples, (successor,
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action, stepCost), where 'successor' is a successor to the current
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state, 'action' is the action required to get there, and 'stepCost' is
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the incremental cost of expanding to that successor.
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"""
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util.raiseNotDefined()
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def getCostOfActions(self, actions):
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"""
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actions: A list of actions to take
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This method returns the total cost of a particular sequence of actions.
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The sequence must be composed of legal moves.
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"""
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util.raiseNotDefined()
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def tinyMazeSearch(problem):
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"""
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Returns a sequence of moves that solves tinyMaze. For any other maze, the
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sequence of moves will be incorrect, so only use this for tinyMaze.
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"""
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from game import Directions
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s = Directions.SOUTH
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w = Directions.WEST
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return [s, s, w, s, w, w, s, w]
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def depthFirstSearch(problem):
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"""
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Search the deepest nodes in the search tree first.
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Your search algorithm needs to return a list of actions that reaches the
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goal. Make sure to implement a graph search algorithm.
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To get started, you might want to try some of these simple commands to
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understand the search problem that is being passed in:
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print "Start:", problem.getStartState()
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print "Is the start a goal?", problem.isGoalState(problem.getStartState())
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print "Start's successors:", problem.getSuccessors(problem.getStartState())
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"""
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"*** YOUR CODE HERE ***"
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maPile = util.Stack()
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maListeFermee = []
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# Verifier si le depart est un but
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if problem.isGoalState(problem.getStartState()):
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return []
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# Mettre le premier noeud et un chemin vide dans la pile
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maPile.push((problem.getStartState(),list()))
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# Rechercher tant que la pile n'est pas vide
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while (not maPile.isEmpty()):
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# Extraire le noeud courant
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noeudCourant, listeDirections = maPile.pop()
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# print "Noeud courant: ", noeudCourant
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# La marquer comme visite
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maListeFermee.append(noeudCourant)
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# Verifier si c'est un but
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if(problem.isGoalState(noeudCourant)):
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# print "Nous avons atteint le but !"
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return listeDirections
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# Effectuer la recherche de successeurs
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for s in problem.getSuccessors(noeudCourant):
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# Decomposer le tuple pour faciliter la lecture
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succNoeudCourant, succDirection, succCost = s
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# Verifier si le noeud n'est pas deja visite et n'est pas dans la pile
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if (succNoeudCourant not in maListeFermee):
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# Dans ce cas, ajouter le noeud et le chemin pour s'y rendre a la pile
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# print "Ajout du noeud a la pile: ", succNoeudCourant
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maPile.push((succNoeudCourant,listeDirections+[succDirection]))
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# print "pas de solution"
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util.raiseNotDefined()
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def breadthFirstSearch(problem):
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"""Search the shallowest nodes in the search tree first."""
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"*** YOUR CODE HERE ***"
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maFile = util.Queue()
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maListeFermee = []
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# Verifier si le depart est un but
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if problem.isGoalState(problem.getStartState()):
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return []
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# Mettre le premier noeud et un chemin vide dans la file
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maFile.push((problem.getStartState(),list()))
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while (not maFile.isEmpty()):
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# Extraire le noeud courant
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noeudCourant, listeDirections = maFile.pop()
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#print "Noeud courant: ", noeudCourant
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# La marquer comme visite
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maListeFermee.append(noeudCourant)
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# Verifier si c'est un but
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if(problem.isGoalState(noeudCourant)):
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#print "Nous avons atteint le but !"
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return listeDirections
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# Effectuer la recherche de successeurs
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for s in problem.getSuccessors(noeudCourant):
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# Decomposer le tuple pour faciliter la lecture
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succNoeudCourant, succDirection, succCost = s
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# Verifier si le noeud n'est pas deja visite et n'est pas dans la file
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if (succNoeudCourant not in maListeFermee+[i[0] for i in maFile.list]):
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# Dans ce cas, ajouter le noeud et le chemin pour s'y rendre a la file
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#print "Ajout du noeud a la file: ", succNoeudCourant
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maFile.push((succNoeudCourant,listeDirections+[succDirection]))
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# print "pas de solution"
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util.raiseNotDefined()
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def uniformCostSearch(problem):
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"""Search the node of least total cost first."""
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"*** YOUR CODE HERE ***"
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maFileEtat = util.PriorityQueue()
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# Je fais une 2e file parce que sinon je ne peux pas comparer le cout
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# si je le met dans le meme tuple que le chemin
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maFileChemin = util.PriorityQueue()
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maListeFermee = []
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# Verifier si le depart est un but
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if problem.isGoalState(problem.getStartState()):
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return []
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# Mettre le premier noeud dans la file
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maFileEtat.push(problem.getStartState(),0)
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# Extraire le noeud courant
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listeDirections = []
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while (not maFileEtat.isEmpty()):
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noeudCourant = maFileEtat.pop()
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#print "Noeud courant: ", noeudCourant
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# La marquer comme visite
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if noeudCourant not in maListeFermee:
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maListeFermee.append(noeudCourant)
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# Verifier si c'est un but
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if(problem.isGoalState(noeudCourant)):
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#print "Nous avons atteint le but !"
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return listeDirections
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# Effectuer la recherche de successeurs
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for s in problem.getSuccessors(noeudCourant):
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# Decomposer le tuple pour faciliter la lecture
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succNoeudCourant, succDirection, succCost = s
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# Verifier si le noeud n'est pas deja visite
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succCostChemin = None
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if (succNoeudCourant not in maListeFermee+[i[0] for i in maFileEtat.heap]):
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# obtenir le cout total du chemin
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succCostChemin = problem.getCostOfActions(listeDirections+[succDirection])
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# Si le noeud est encore dans la file, mettre a jour la priorite si elle devient inferieure
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#print "Ajout du noeud a la file: ", succNoeudCourant
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maFileEtat.push(succNoeudCourant,succCostChemin)
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# Ajouter le nouveau chemin dans la file avec son cout
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maFileChemin.push(listeDirections+[succDirection],succCostChemin)
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# si noeud existant, mettre a jour le cout si meilleur
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elif succNoeudCourant in [i[0] for i in maFileEtat.heap]:
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maFileEtat.update(succNoeudCourant,succCostChemin)
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listeDirections = maFileChemin.pop()
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# print "pas de solution"
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util.raiseNotDefined()
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def nullHeuristic(state, problem=None):
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"""
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A heuristic function estimates the cost from the current state to the nearest
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goal in the provided SearchProblem. This heuristic is trivial.
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"""
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return 0
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def aStarSearch(problem, heuristic=nullHeuristic):
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"""Search the node that has the lowest combined cost and heuristic first."""
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"*** YOUR CODE HERE ***"
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maFileEtat = util.PriorityQueue()
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# Je fais une 2e file parce que sinon je ne peux pas comparer le cout
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# si je le met dans le meme tuple que le chemin
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maFileChemin = util.PriorityQueue()
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maListeFermee = []
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# Verifier si le depart est un but
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if problem.isGoalState(problem.getStartState()):
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return []
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# Mettre le premier noeud dans la file
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maFileEtat.push(problem.getStartState(),0)
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# Extraire le noeud courant
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listeDirections = []
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while (not maFileEtat.isEmpty()):
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noeudCourant = maFileEtat.pop()
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# print "Noeud courant: ", noeudCourant
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# La marquer comme visite
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if noeudCourant not in maListeFermee:
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maListeFermee.append(noeudCourant)
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# Verifier si c'est un but
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if(problem.isGoalState(noeudCourant)):
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#print "Nous avons atteint le but !"
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return listeDirections
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# Effectuer la recherche de successeurs
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for s in problem.getSuccessors(noeudCourant):
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# Decomposer le tuple pour faciliter la lecture
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succ_noeud_courant, succDirection, succCost = s
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# Verifier si le noeud n'est pas deja visite
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succ_cost_chemin = None
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if succ_noeud_courant not in maListeFermee+[i[0] for i in maFileEtat.heap]:
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# obtenir le cout total du chemin
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succ_cost_chemin = problem.getCostOfActions(listeDirections+[succDirection])+heuristic(succ_noeud_courant,problem)
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# Si le noeud est encore dans la file, mettre a jour la priorite si elle devient inferieure
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# print "Ajout du noeud a la file: ", succ_noeud_courant
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maFileEtat.push(succ_noeud_courant,succ_cost_chemin)
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# Ajouter le nouveau chemin dans la file avec son cout
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maFileChemin.push(listeDirections+[succDirection],succ_cost_chemin)
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# si noeud existant, mettre a jour le cout si meilleur
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elif succ_noeud_courant in [i[0] for i in maFileEtat.heap]:
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maFileEtat.update(succ_noeud_courant,succ_cost_chemin)
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listeDirections = maFileChemin.pop()
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# print "pas de solution"
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util.raiseNotDefined()
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# Abbreviations
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bfs = breadthFirstSearch
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dfs = depthFirstSearch
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astar = aStarSearch
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ucs = uniformCostSearch
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