225 lines
7.2 KiB
Python
225 lines
7.2 KiB
Python
"""
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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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Please only change the parts of the file you are asked to. Look for the lines
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that say
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"*** YOUR CODE HERE ***"
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Follow the project description for details.
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Good luck and happy searching!
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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: SearchProblem):
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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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path = []
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list()
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stack = util.Stack()
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stack.push(((curr_state, 'None', 0), path)) # (state, path)
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while not isGoal:
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if stack.isEmpty():
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return []
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else:
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((new_state, direction, cost), path) = stack.pop() # ((location, direction, cost), path)
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if new_state in visited:
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continue
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path = path + [direction]
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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if isGoal:
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break
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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stack.push(((_state, _direction, _cost), path))
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path = path[1:]
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return path
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def breadthFirstSearch(problem: SearchProblem):
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"""Search the shallowest nodes in the search tree first."""
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"*** YOUR CODE HERE ***"
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path = []
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list()
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queue = util.Queue()
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queue.push(((curr_state, 'None', 0), path)) # (state, path)
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while not isGoal:
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if queue.isEmpty():
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return []
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else:
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((new_state, direction, cost), path) = queue.pop() # ((location, direction, cost), path)
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if new_state in visited:
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continue
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path = path + [direction]
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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if isGoal:
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break
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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queue.push(((_state, _direction, _cost), path))
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path = path[1:]
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return path
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def uniformCostSearch(problem: SearchProblem):
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"""Search the node of least total cost first."""
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"*** YOUR CODE HERE ***"
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path = []
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costs = 0
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list()
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queue = util.PriorityQueue()
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queue.push(((curr_state, 'None', 0), path, costs), 0) # (state, path)
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while not isGoal:
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if queue.isEmpty():
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return []
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else:
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((new_state, direction, cost), path, costs) = queue.pop() # ((location, direction, cost), path)
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if new_state in visited:
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continue
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path = path + [direction]
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costs = costs + cost
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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if isGoal:
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break
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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queue.push(((_state, _direction, _cost), path, costs), costs+_cost)
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path = path[1:]
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return path
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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: SearchProblem, 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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path = []
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costs = 0
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list()
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queue = util.PriorityQueue()
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queue.push(((curr_state, 'None', 0), path, costs), 0) # (state, path)
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min_cost = {curr_state: 0}
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while not isGoal:
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if queue.isEmpty():
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return []
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else:
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((new_state, direction, cost), path, costs) = queue.pop() # ((location, direction, cost), path)
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if new_state in visited and (costs+cost)>=min_cost[new_state]:
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continue
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path = path + [direction]
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costs = costs + cost
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min_cost[new_state] = costs
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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if isGoal:
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break
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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queue.push(((_state, _direction, _cost), path, costs), costs+_cost+heuristic(_state, problem))
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path = path[1:]
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return path
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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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