# 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
# ([email protected]) and Dan Klein ([email protected]).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel ([email protected]).
"""
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
fringe = Stack()
closed = []
fringe.push((problem.getStartState(), []))
while not fringe.isEmpty():
cur_node, actions = fringe.pop()
if problem.isGoalState(cur_node):
return actions
if cur_node not in closed:
expand = problem.getSuccessors(cur_node)
closed.append(cur_node)
for location, direction, cost in expand:
if (location not in closed):
fringe.push((location, actions + [direction]))
util.raiseNotDefined()
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
"*** YOUR CODE HERE ***"
from util import Queue
from game import Directions
fringe = Queue()
closed = []
fringe.push((problem.getStartState(), []))
while not fringe.isEmpty():
cur_node, actions = fringe.pop()
if problem.isGoalState(cur_node):
return actions
if cur_node not in closed:
expand = problem.getSuccessors(cur_node)
closed.append(cur_node)
for location, direction, cost in expand:
if (location not in closed):
fringe.push((location, actions + [direction]))
util.raiseNotDefined()
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
"*** YOUR CODE HERE ***"
start_point = problem.getStartState()
queue = util.PriorityQueueWithFunction(lambda x: x[2])
queue.push((start_point,None,0))
cost=0
visited = []
path = []
parentSeq = {}
parentSeq[(start_point,None,0)]=None
while queue.isEmpty() == False:
current_fullstate = queue.pop()
#print current_fullstate
if (problem.isGoalState(current_fullstate[0])):
break
else:
current_state = current_fullstate[0]
if current_state not in visited:
visited.append(current_state)
else:
continue
successors = problem.getSuccessors(current_state)
for state in successors:
cost= current_fullstate[2] + state[2];
#print state,cost
if state[0] not in visited:
queue.push((state[0],state[1],cost))
#parentSeq[state] = current_fullstate
parentSeq[(state[0],state[1])] = current_fullstate
child = current_fullstate
while (child != None):
path.append(child[1])
if child[0] != start_point:
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
fringe = util.PriorityQueue()
actions = []
fringe.push((problem.getStartState(),actions),0)
visited = []
tmpActions = []
while fringe:
currState,actions = fringe.pop()
if problem.isGoalState(currState):
break
if currState not in visited:
visited.append(currState)
successors = problem.getSuccessors(currState)
for successor, action, cost in successors:
tempActions = actions + [action]
nextCost = problem.getCostOfActions(tempActions) + heuristic(successor,problem)
if successor not in visited:
fringe.push((successor,tempActions),nextCost)
return actions
#util.raiseNotDefined()
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch