248 lines
9.2 KiB
Python
248 lines
9.2 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 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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# 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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action_list=[]
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# action_list=["South", "South", "West", "South", "West", "West", "South", "West"]
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vis_stk=util.Stack()
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has_visited={}
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vis_stk.push(problem.getStartState())
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# print(f'push {problem.getStartState()}')
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has_visited[problem.getStartState()]=True
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nex_idx={}
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nex_idx[problem.getStartState()]=0
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best_actions={}
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best_actions[problem.getStartState()]=[]
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stored_successors={}
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def SafelyFetchSuccessors(problem,stored_successors,state):
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if state in stored_successors:
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return stored_successors[state]
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else:
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stored_successors[state]=problem.getSuccessors(state)
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return stored_successors[state]
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while True:
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if vis_stk.isEmpty():
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break
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cur_state=vis_stk.pop()
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# print(f'pop {cur_state}')
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cur_actions=best_actions[cur_state]
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if problem.isGoalState(cur_state):
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action_list=cur_actions
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break
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vis_stk.push(cur_state)
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# print(f'push {cur_state}')
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# successors=problem.getSuccessors(cur_state)
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successors=SafelyFetchSuccessors(problem,stored_successors,cur_state)
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# print(f"getting successors of {cur_state} with {successors}")
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while nex_idx[cur_state]>=len(successors):
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tmp=vis_stk.pop()
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# print(f'pop {tmp}')
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if vis_stk.isEmpty():
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break
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cur_state=vis_stk.pop()
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# print(f'pop {cur_state}')
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cur_actions=best_actions[cur_state]
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vis_stk.push(cur_state)
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# print(f'push {cur_state}')
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# successors=problem.getSuccessors(cur_state)
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successors=SafelyFetchSuccessors(problem,stored_successors,cur_state)
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# print(f'getting successors of {cur_state} with {successors}')
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if vis_stk.isEmpty():
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break
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next_state,action,_=successors[nex_idx[cur_state]]
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nex_idx[cur_state]+=1
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if next_state not in has_visited:
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vis_stk.push(next_state)
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# print(f'push {next_state}')
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best_actions[next_state]=cur_actions+[action]
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has_visited[next_state]=True
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nex_idx[next_state]=0
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return action_list
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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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# 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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action_list=[]
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vis_que=util.Queue()
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has_visited={}
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vis_que.push((problem.getStartState(),[]))
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has_visited[problem.getStartState()]=True
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while True:
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if vis_que.isEmpty():
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break
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cur_state, cur_actions=vis_que.pop()
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if problem.isGoalState(cur_state):
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action_list=cur_actions
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break
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for next_state,action,_ in problem.getSuccessors(cur_state):
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if next_state not in has_visited:
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vis_que.push((next_state,cur_actions+[action]))
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has_visited[next_state]=True
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return action_list
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def uniformCostSearch(problem: SearchProblem):
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"""Search the node of least total cost first."""
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action_list=[]
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vis_que=util.PriorityQueue()
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has_visited={}
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vis_que.push(problem.getStartState(),0)
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dis={}
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dis[problem.getStartState()]=0
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best_actions={}
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best_actions[problem.getStartState()]=[]
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while True:
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if vis_que.isEmpty():
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break
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cur_state=vis_que.pop()
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cur_actions=best_actions[cur_state]
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# print("cur_state:",cur_state, "cur cost=",dis[cur_state])
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if problem.isGoalState(cur_state):
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action_list=cur_actions
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# print("minimal cost:",dis[cur_state])
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break
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if not cur_state in has_visited:
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for next_state,action,cost in problem.getSuccessors(cur_state):
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# print(f"next_state={next_state}")
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# print(f"try update {next_state} with cost={dis[cur_state]+cost}")
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if dis[cur_state]+cost<dis.get(next_state,1e20):
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vis_que.update(next_state,dis[cur_state]+cost)
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dis[next_state]=min(dis.get(next_state,1e20),dis[cur_state]+cost)
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best_actions[next_state]=cur_actions+[action]
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has_visited[cur_state]=True
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return action_list
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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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kInf=1e100
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action_list=[]
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vis_que=util.PriorityQueue()
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has_visited={}
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vis_que.push(problem.getStartState(),heuristic(problem.getStartState(),problem))
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dis={}
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dis[problem.getStartState()]=0
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best_actions={}
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best_actions[problem.getStartState()]=[]
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while True:
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if vis_que.isEmpty():
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break
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cur_state=vis_que.pop()
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cur_actions=best_actions[cur_state]
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# print("cur_state:",cur_state, "cur cost=",dis[cur_state])
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if problem.isGoalState(cur_state):
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action_list=cur_actions
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# print("minimal cost:",dis[cur_state])
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break
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if not cur_state in has_visited:
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for next_state,action,cost in problem.getSuccessors(cur_state):
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# print(f"next_state={next_state}")
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# print(f"try update {next_state} with cost={dis[cur_state]+cost}")
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if dis[cur_state]+cost<dis.get(next_state,kInf):
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vis_que.update(next_state,dis[cur_state]+cost+heuristic(next_state,problem))
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dis[next_state]=min(dis.get(next_state,kInf),dis[cur_state]+cost)
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best_actions[next_state]=cur_actions+[action]
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has_visited[cur_state]=True
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return action_list
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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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