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212 lines
7.0 KiB
212 lines
7.0 KiB
# -*- 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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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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from util import Stack
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from game import Directions
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frontier=Stack()
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visited=[]
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frontier.push((problem.getStartState(),[]))
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while not frontier.isEmpty():
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cur_node,actions=frontier.pop()
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if problem.isGoalState(cur_node):
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return actions
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if cur_node not in visited:
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expand=problem.getSuccessors(cur_node)
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visited.append(cur_node)
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for location,direction,cost in expand:
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if location not in visited:
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frontier.push((location,actions+[direction]))
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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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frontier=util.Queue()
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visited=[]
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frontier.push((problem.getStartState(),[]))
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while not frontier.isEmpty():
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cur_node,actions=frontier.pop()
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if problem.isGoalState(cur_node):
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return actions
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if cur_node not in visited:
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expand=problem.getSuccessors(cur_node)
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visited.append(cur_node)
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for location,direction,cost in expand:
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if location not in visited:
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frontier.push((location,actions+[direction]))
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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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frontier = util.PriorityQueueWithFunction(lambda x: x[2])
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visited=[]
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frontier.push((problem.getStartState(),None,0))
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path = []
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parentSeq = {}
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parentSeq[(problem.getStartState(),None,0)]=None
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while not frontier.isEmpty():
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current_fullstate=frontier.pop()
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# print(current_fullstate)
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cur_node=current_fullstate[0]
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actions=current_fullstate[1]
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if problem.isGoalState(cur_node):
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break
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if cur_node not in visited:
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expand=problem.getSuccessors(cur_node)
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visited.append(cur_node)
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for state in expand:
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location = state[0]
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direction = state[1]
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cost=current_fullstate[2]+state[2]
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if location not in visited:
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frontier.push((location,direction,cost))
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parentSeq[(location,direction)] = current_fullstate
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# elif location in visited:
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# frontier.update((location,direction,cost))
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child = current_fullstate
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while (child != None):
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path.append(child[1])
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if child[0] != problem.getStartState():
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child = parentSeq[(child[0],child[1])]
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else:
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child = None
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path.reverse()
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return path[1:]
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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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from sets import Set
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frontier = util.PriorityQueue()
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actions = []
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frontier.push((problem.getStartState(),actions),0)
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visited = []
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while frontier:
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cur_node,actions = frontier.pop()
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if problem.isGoalState(cur_node):
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return actions
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if cur_node not in visited:
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visited.append(cur_node)
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expand = problem.getSuccessors(cur_node)
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for successor, action, cost in expand:
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tempActions = actions + [action]
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nextCost = problem.getCostOfActions(tempActions) + heuristic(successor,problem)
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if successor not in visited:
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frontier.push((successor,tempActions),nextCost)
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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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