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# -*- coding:utf-8 -*-
# search.py
# ---------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""
import util
class SearchProblem:
"""
This class outlines the structure of a search problem, but doesn't implement
any of the methods (in object-oriented terminology: an abstract class).
You do not need to change anything in this class, ever.
"""
def getStartState(self):
"""
Returns the start state for the search problem.
"""
util.raiseNotDefined()
def isGoalState(self, state):
"""
state: Search state
Returns True if and only if the state is a valid goal state.
"""
util.raiseNotDefined()
def getSuccessors(self, state):
"""
state: Search state
For a given state, this should return a list of triples, (successor,
action, stepCost), where 'successor' is a successor to the current
state, 'action' is the action required to get there, and 'stepCost' is
the incremental cost of expanding to that successor.
"""
util.raiseNotDefined()
def getCostOfActions(self, actions):
"""
actions: A list of actions to take
This method returns the total cost of a particular sequence of actions.
The sequence must be composed of legal moves.
"""
util.raiseNotDefined()
def tinyMazeSearch(problem):
"""
Returns a sequence of moves that solves tinyMaze. For any other maze, the
sequence of moves will be incorrect, so only use this for tinyMaze.
"""
from game import Directions
s = Directions.SOUTH
w = Directions.WEST
return [s, s, w, s, w, w, s, w]
def depthFirstSearch(problem):
"""
Search the deepest nodes in the search tree first.
Your search algorithm needs to return a list of actions that reaches the
goal. Make sure to implement a graph search algorithm.
To get started, you might want to try some of these simple commands to
understand the search problem that is being passed in:
print "Start:", problem.getStartState()
print "Is the start a goal?", problem.isGoalState(problem.getStartState())
print "Start's successors:", problem.getSuccessors(problem.getStartState())
"""
"*** YOUR CODE HERE ***"
from util import Stack
from game import Directions
frontier=Stack()
visited=[]
frontier.push((problem.getStartState(),[]))
while not frontier.isEmpty():
cur_node,actions=frontier.pop()
if problem.isGoalState(cur_node):
return actions
if cur_node not in visited:
expand=problem.getSuccessors(cur_node)
visited.append(cur_node)
for location,direction,cost in expand:
if location not in visited:
frontier.push((location,actions+[direction]))
util.raiseNotDefined()
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
frontier=util.Queue()
visited=[]
frontier.push((problem.getStartState(),[]))
while not frontier.isEmpty():
cur_node,actions=frontier.pop()
if problem.isGoalState(cur_node):
return actions
if cur_node not in visited:
expand=problem.getSuccessors(cur_node)
visited.append(cur_node)
for location,direction,cost in expand:
if location not in visited:
frontier.push((location,actions+[direction]))
util.raiseNotDefined()
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
frontier = util.PriorityQueueWithFunction(lambda x: x[2])
visited=[]
frontier.push((problem.getStartState(),None,0))
path = []
parentSeq = {}
parentSeq[(problem.getStartState(),None,0)]=None
while not frontier.isEmpty():
current_fullstate=frontier.pop()
# print(current_fullstate)
cur_node=current_fullstate[0]
actions=current_fullstate[1]
if problem.isGoalState(cur_node):
break
if cur_node not in visited:
expand=problem.getSuccessors(cur_node)
visited.append(cur_node)
for state in expand:
location = state[0]
direction = state[1]
cost=current_fullstate[2]+state[2]
if location not in visited:
frontier.push((location,direction,cost))
parentSeq[(location,direction)] = current_fullstate
# elif location in visited:
# frontier.update((location,direction,cost))
child = current_fullstate
while (child != None):
path.append(child[1])
if child[0] != problem.getStartState():
child = parentSeq[(child[0],child[1])]
else:
child = None
path.reverse()
return path[1:]
util.raiseNotDefined()
def nullHeuristic(state, problem=None):
"""
A heuristic function estimates the cost from the current state to the nearest
goal in the provided SearchProblem. This heuristic is trivial.
"""
return 0
def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
"*** YOUR CODE HERE ***"
from sets import Set
frontier = util.PriorityQueue()
actions = []
frontier.push((problem.getStartState(),actions),0)
visited = []
while frontier:
cur_node,actions = frontier.pop()
if problem.isGoalState(cur_node):
return actions
if cur_node not in visited:
visited.append(cur_node)
expand = problem.getSuccessors(cur_node)
for successor, action, cost in expand:
tempActions = actions + [action]
nextCost = problem.getCostOfActions(tempActions) + heuristic(successor,problem)
if successor not in visited:
frontier.push((successor,tempActions),nextCost)
util.raiseNotDefined()
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch