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search.py
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search.py
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# search.py
# ---------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
"""
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]
class Node:
def __init__(self, state, parent, action, stepcost):
self.state = state
self.parent = parent
self.action = action
if parent==None:
self.cost = stepcost
else:
self.cost = parent.cost + stepcost
def __str__(self):
return "State: " + str(self.state) + "\n" + \
"Parent: " + str(self.parent.state) + "\n" + \
"Action: " + str(self.action) + "\n" + \
"Cost: " + str(self.cost)
def getState(self):
return self.state
def getParent(self):
return self.parent
def getAction(self):
return self.action
def getCost(self):
return self.cost
def pathFromStart(self):
stateList = []
actionList = []
currNode = self
while currNode.getAction() is not None:
#print stateList
#print actionList
stateList.append(currNode.getState())
actionList.append(currNode.getAction())
currNode = currNode.parent
actionList.reverse()
return actionList
def depthFirstSearch(problem):
"""
Search the deepest nodes in the search tree first
[2nd Edition: p 75, 3rd Edition: p 87]
Your search algorithm needs to return a list of actions that reaches
the goal. Make sure to implement a graph search algorithm
[2nd Edition: Fig. 3.18, 3rd Edition: Fig 3.7].
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())
"""
#Queue - BFS
s = util.Stack()
explored = []
startNode = Node(problem.getStartState(), None, None, 0)
s.push(startNode)
actionslist = []
while (not s.isEmpty()):
currNode = s.pop()
explored.append(currNode.getState())
if problem.isGoalState(currNode.getState()):
print "done"
return currNode.pathFromStart()
else:
successors = problem.getSuccessors(currNode.getState())
for item in successors:
state = item[0]
action = item[1]
stepcost = item[2]
if state not in explored:
s.push( Node(state, currNode, action, stepcost) )
util.raiseNotDefined()
def breadthFirstSearch(problem):
"""
Search the shallowest nodes in the search tree first.
[2nd Edition: p 73, 3rd Edition: p 82]
"""
s = util.Queue()
explored = []
startNode = Node(problem.getStartState(), None, None, 0)
s.push(startNode)
actionslist = []
while (not s.isEmpty()):
#for i in range(3):
currNode = s.pop()
#explored[currNode.getState()] = 1
explored.append(currNode.getState())
#print "Currnode", currNode.getState()
if problem.isGoalState(currNode.getState()):
print "done"
return currNode.pathFromStart()
else:
successors = problem.getSuccessors(currNode.getState())
for item in successors:
state = item[0]
action = item[1]
stepcost = item[2]
if state not in explored:
s.push( Node(state, currNode, action, stepcost) )
util.raiseNotDefined()
class NodeUCS:
def __init__(self, state, parent, action):
self.state = state
self.parent = parent
self.action = action
if parent==None:
self.actionsToReachNode = []
else:
t = parent.actionsToReachNode[:]
t.append(action)
self.actionsToReachNode = t
def __str__(self):
return "State: " + str(self.state) + "\n" + \
"Parent: " + str(self.parent.state) + "\n" + \
"Action: " + str(self.action) + "\n" + \
"Cost: " + str(self.cost)
def getState(self):
return self.state
def getParent(self):
return self.parent
def getAction(self):
return self.action
def getActionsToReachNode(self):
return self.actionsToReachNode
def uniformCostSearch(problem):
"Search the node of least total cost first. "
s = util.PriorityQueue()
explored = []
startNode = NodeUCS(problem.getStartState(), None, None)
s.push(startNode, problem.getCostOfActions(startNode.actionsToReachNode))
while (not s.isEmpty()):
#for i in range(3):
currNode = s.pop()
explored.append(currNode.getState())
if problem.isGoalState(currNode.getState()):
print "done"
return currNode.getActionsToReachNode()
else:
successors = problem.getSuccessors(currNode.getState())
for item in successors:
state = item[0]
action = item[1]
if state not in explored:
n = NodeUCS(state, currNode, action)
#print "Action sequence: ", n.getActionsToReachNode()
#util.pause()
s.push( n, problem.getCostOfActions(n.getActionsToReachNode() ))
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 ***"
s = util.PriorityQueue()
explored = []
startNode = NodeUCS(problem.getStartState(), None, None)
s.push(startNode, problem.getCostOfActions(startNode.actionsToReachNode) + heuristic(startNode.getState(),problem))
while (not s.isEmpty()):
#for i in range(3):
currNode = s.pop()
explored.append(currNode.getState())
if problem.isGoalState(currNode.getState()):
print "done"
return currNode.getActionsToReachNode()
else:
successors = problem.getSuccessors(currNode.getState())
for item in successors:
state = item[0]
action = item[1]
if state not in explored:
n = NodeUCS(state, currNode, action)
#print "Action sequence: ", n.getActionsToReachNode()
#util.pause()
s.push( n, problem.getCostOfActions(n.getActionsToReachNode() ) + heuristic(n.getState(),problem))
util.raiseNotDefined()
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch