# 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 next_node:”, problem.getSuccessors(problem.getStartState()))
“””
“*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***”
from util import Stack
from game import Directions
open = Stack()
closed = []
open.push((problem.getStartState(), []))
while not open.isEmpty():
current_node, actions = open.pop()
# if current state is the goal state
# return list of actions
if problem.isGoalState(current_node):
return actions
if current_node not in closed:
# expand current node
# add current node to closed list
expand = problem.getSuccessors(current_node)
closed.append(current_node)
for location, direction, cost in expand:
# if the location has not been visited, put into open list
if (location not in closed):
open.push((location, actions + [direction]))
util.raiseNotDefined()
def breadthFirstSearch(problem):
“””Search the shallowest nodes in the search tree first.”””
“*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***”
from util import Queue
from game import Directions
open = Queue()
closed = []
open.push((problem.getStartState(), []))
while not open.isEmpty():
current_node, actions = open.pop()
# if current state is the goal state
# return list of actions
if problem.isGoalState(current_node):
return actions
if current_node not in closed:
expand = problem.getSuccessors(current_node)
closed.append(current_node)
for location, direction, cost in expand:
# if the location has not been visited, put into open list
if (location not in closed):
open.push((location, actions + [direction]))
util.raiseNotDefined()
def uniformCostSearch(problem):
“””Search the node of least total cost first.”””
“*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***”
queue = util.PriorityQueueWithFunction(lambda x: x[2])
visited = []
actions = []
cost = 0
start = problem.getStartState()
queue.push((start, None, cost))
parents = {}
parents[(start, None, 0)] = None
while not queue.isEmpty():
current_node = queue.pop()
if problem.isGoalState(current_current_node):
break
else:
current_node_state = current_current_node
if current_node_state not in visited:
visited.append(current_node_state)
else:
continue
expand = problem.getSuccessors(current_node_state)
for state in expand:
cost = current_node[2] + state[2]
# if the location has not been visited, put into open list
if (state[0] not in visited):
queue.push((state[0], state[1], cost))
parents[(state[0], state[1])] = current_node
child = current_node
while (child != None):
actions.append(child[1])
if location != start:
child = parents[(location, child[1])]
else:
child = None
actions.reverse()
return actions[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 IF YOU WANT TO PRACTICE ***”
from util import Queue
from game import Directions
open = util.PriorityQueue()
closed = []
cost = 0
actions = []
open.push((problem.getStartState(), []), 0)
while not open.isEmpty():
current_node, actions = open.pop()
if problem.isGoalState(current_node):
return actions
if current_node not in closed:
expand = problem.getSuccessors(current_node)
closed.append(current_node)
for location, direction, tmp_cost in expand:
cost = problem.getCostOfActions(actions +[direction]) + heuristic(location,problem)
if (location not in closed):
open.push((location, actions + [direction]),cost)
return actions
util.raiseNotDefined()
def iterativeDeepeningSearch(problem):
“””Search the deepest node in an iterative manner.”””
“*** YOUR CODE HERE FOR TASK 1 ***”
from util import Stack
open = Stack()
limit = 1
while open.isEmpty():
open.push(([problem.getStartState()], [], 0))
while not open.isEmpty():
(visited_nodes, actions, cost) = open.pop()
current_node = visited_nodes[-1]
if problem.isGoalState(current_node):
return actions
elif len(visited_nodes) < limit:
next_node = problem.getSuccessors(current_node)
for (location, direction, next_cost) in next_node:
if location not in visited_nodes:
open.push((visited_nodes + [location], actions + [direction], cost + next_cost))
limit += 1
return actions
def waStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has has the weighted (x 2) lowest combined cost and heuristic first."""
"*** YOUR CODE HERE FOR TASK 2 ***"
from util import PriorityQueue
open = PriorityQueue()
closed = []
w = 2
weighted_h = w * heuristic(problem.getStartState(), problem)
open.push((problem.getStartState(), [], 0), weighted_h)
(current_node, actions, cost) = open.pop()
closed.append((current_node, cost + heuristic(problem.getStartState(), problem)))
while not problem.isGoalState(current_node):
next_node = problem.getSuccessors(current_node)
for location, direction, next_cost in next_node:
visited = False
for (visited_node, visited_cost) in closed:
if (location == visited_node) and (cost + next_cost >= visited_cost):
visited = True
break
if not visited:
new_state = (location, actions + [direction], cost + next_cost)
open.push(new_state, cost + next_cost + heuristic(location, problem))
closed.append((location, cost + next_cost))
(current_node, actions, cost) = open.pop()
return actions
util.raiseNotDefined()
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
ids = iterativeDeepeningSearch
wastar = waStarSearch