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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
from game import Directions
from spade import pyxf
import re
import sys
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 [p 85].
Your search algorithm needs to return a list of actions that reaches
the goal. Make sure to implement a graph search algorithm [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())
"""
"*** YOUR CODE HERE ***"
"""
This function makes a generic implementation of DFS.Following variables are mainly used -
1. state_stack - It is the stack class imported from util class. It keeps track of the nodes to be expanded and pops the nodes depth first.
2. parents - It is used to backtrack the path of the node after it reaches the goal.
3. direction - This is also used to backtrack the path of the node once it reaches goal. It gives the exact direction to trace back.
4. visited - This keeps track of visited nodes so as to avoid a deadlock.
5. path - This is used to return the path to the main function
"""
print "***************************DFS Start***************************"
south = Directions.SOUTH
west = Directions.WEST
north = Directions.NORTH
east = Directions.EAST
print "DFS XSB Setting Starts"
myXSB = pyxf.xsb("/home/gp/Downloads/XSB/bin/xsb")
myXSB.load("maze.P")
myXSB.load("dfs.P")
print "DFS XSB Setting Starts"
result = myXSB.query("connected(start, A,D).")
result1 = myXSB.query("dfs_search(start,[],P,D).")
path = result1[0]['D']
path2 = path[1:-6]
path3 = re.split(',',path2)
path4 = [word[:1].upper() + word[1:] for word in path3]
#path4 = filter(lambda a:a!="South",path4)
print path4
print "***************************DFS End***************************"
print "Sending the path"
return path4
def breadthFirstSearch(problem):
"Search the shallowest nodes in the search tree first. [p 81]"
"*** YOUR CODE HERE ***"
"""
This function makes a generic implementation of BFS.Following variables are mainly used -
1. state_queue - It is the queue class imported from util class. It keeps track of the nodes to be expanded and pops the nodes breadth first.
2. parents - It is used to backtrack the path of the node after it reaches the goal.
3. direction - This is also used to backtrack the path of the node once it reaches goal. It gives the exact direction to trace back.
4. visited - This keeps track of visited nodes so as to avoid a deadlock.
5. path - This is used to return the path to the main function
"""
south = Directions.SOUTH
west = Directions.WEST
north = Directions.NORTH
east = Directions.EAST
print "***************************BFS Start***************************"
print "BFS XSB Setting Starts"
myXSB = pyxf.xsb("/home/gp/Downloads/XSB/bin/xsb")
myXSB.load("maze.P")
myXSB.load("bfs.P")
print "BFS XSB Setting Ends"
print "******BFS Query Starts******"
result = myXSB.query("connected(start, A,D).")
result1 = myXSB.query("bfs_search(start,D).")
print "******BFS Query Ends******"
path = result1[0]['D']
path2 = path[1:-7]
path3 = re.split(',',path2)
path4 = [word[:1].upper() + word[1:] for word in path3]
path5 = path4[1:][::2]
path6 = path5[::-1]
print path6
print "***************************BFS Ends***************************"
return path6
def uniformCostSearch(problem):
"Search the node of least total cost first. "
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
def nullHeuristic(state, problem=None):
print "***************************nullHeuristic()***************************"
"""
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):
print "***************************astarSearch Start***************************"
south = Directions.SOUTH
west = Directions.WEST
north = Directions.NORTH
east = Directions.EAST
myXSB = pyxf.xsb("/home/gp/Downloads/XSB/bin/xsb")
myXSB.load("mazeastar.P")
myXSB.load("astar.P")
result1 = myXSB.query("astar_search(start, D).")
path = result1[0]['D']
path2 = path[1:-1]
path3 = re.split(',',path2)
final_path = []
i=1
for it in path3:
temp = re.split('#',it.replace(" ",""))
final_path.append(temp[1])
del final_path[-1]
path4 = [word[:1].upper() + word[1:] for word in final_path]
path6 = path4[::-1]
print "***************************astarSearch End***************************"
return path6
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch