154 lines
4.7 KiB
Python
154 lines
4.7 KiB
Python
# 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 genericSearch(problem, getNewCostAndPriority):
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fringe = util.PriorityQueue()
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startState = problem.getStartState()
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fringe.push((startState, [], 0), 0)
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visited = {}
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while True:
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if fringe.isEmpty():
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raise Exception("No path found.")
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state, actions, cost = fringe.pop()
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if problem.isGoalState(state):
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return actions
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if state in visited and cost >= visited[state]:
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continue
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visited[state] = cost
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for successor, action, stepCost in problem.getSuccessors(state):
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newCost, priority = getNewCostAndPriority(cost, stepCost, successor)
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newActions = list(actions) + [action]
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fringe.push((successor, newActions, newCost), priority)
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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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"""
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def getNewCostAndPriority(cost, stepCost, successor):
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newCost = cost + 1
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return newCost, -newCost
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return genericSearch(problem, getNewCostAndPriority)
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def breadthFirstSearch(problem):
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"""Search the shallowest nodes in the search tree first."""
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def getNewCostAndPriority(cost, stepCost, successor):
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newCost = cost + 1
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return newCost, newCost
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return genericSearch(problem, getNewCostAndPriority)
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def uniformCostSearch(problem):
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"""Search the node of least total cost first."""
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def getNewCostAndPriority(cost, stepCost, successor):
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newCost = cost + stepCost
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return newCost, newCost
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return genericSearch(problem, getNewCostAndPriority)
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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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def getNewCostAndPriority(cost, stepCost, successor):
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newCost = cost + stepCost
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newPriority = newCost + heuristic(successor, problem)
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return newCost, newPriority
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return genericSearch(problem, getNewCostAndPriority)
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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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