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search.py
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search.py
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# 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 ["South", s, w, s, w, w, s, w]
"""
****** Depth First Search (DFS)
"""
result_success = "Success"
result_fail = "failed"
def depthFirstSearch(problem):
"""
Search the deepest nodes in the search tree first.
"""
# this will hold all the nodes we need to visit. It follows Last In First Out.
# so whatever node is inserted last, will be picked first to explore.
stack = util.Stack()
# visited list is a list of all visited node.
# In graph, it is possible that same node is connected by multiple branches. This may cause cyclic path.
# To avoid cyclic path of graph, we will check if node is never visited before inserting into the stack
visited = []
# Prepare first node.
# A DfsNode contains
# state (position in terms of X,Y)
# action (direction that requires to reach this node from previous)
starting_state = problem.getStartState() # initial X,Y position
starting_node = DfsNode(starting_state) # starting node with initial position. Action is None for this
result = dfs_explore(problem, stack, starting_node, visited) # starts exploring from start node
if result == result_success: # when goal is found at some depth
return directions_from_stack(stack) # returns list of direction from stack
elif result == result_fail: # when goal is not found after exploring entire graph
print "No solution found"
def dfs_explore(problem, stack, dfs_node, visited):
"""
This function will explore the given dfs_node
if dfs_node is the goal, then it will return result_success
else it will call recursive dfs_explore function for all of it's successor to find goal
:param problem: problem object
:param stack: main stack used to store nodes
:param dfs_node: current node to explore
:param visited: list of visited states
:return: result_success if current node or it's Descendant is goal
result_fail otherwise
"""
# mark current state as visited
visited.append(dfs_node.get_state())
# add node to the stack
stack.push(dfs_node)
# check if current state is goal
if problem.isGoalState(dfs_node.get_state()):
return result_success # return result_success if current node is goal
# if current node is not the goal, go for it's successors' exploration
successors = problem.getSuccessors(dfs_node.get_state())
for successor_state, successor_action, successor_cost in successors:
if successor_state not in visited:
# recursive call for successor
result = dfs_explore(problem, stack, DfsNode(successor_state, successor_action), visited)
if result == result_success:
return result_success
# if no descendant is goal, pop out the item from stack and return result_fail
stack.pop()
return result_fail
def directions_from_stack(stack):
"""
Program expects a list of actions that leads to the goal
For DFS, it can be derived from stack.
This function will prepare list of directions from the nodes of stack
:param stack: it is a stack of nodes [{state:((1,2), action:None},{state:((1,3), action:North},{state:((2,3),
action:East}]
:return: list of directions [North, East]
"""
directions = []
for dfs_node in stack.list:
if dfs_node.get_action() is not None:
directions.append(dfs_node.get_action())
return directions
class DfsNode:
"""
This is a node structure used for DFS problem. It contains state and action to reach that state.
"""
def __init__(self, state, action=None):
self.state = state
self.action = action
def get_state(self):
return self.state
def get_action(self):
return self.action
"""
*** Breadth First Search (BFS)
"""
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
queue = util.Queue()
visited = []
goal_node = None
path = None
# add starting Node as the first node of queue
visited.append(problem.getStartState())
queue.push(BfsNode(problem.getStartState()))
while not queue.isEmpty():
result = bfs_explore(problem, queue, visited)
if result is not None:
goal_node = result
break
if goal_node is not None:
# goal node found successfully.
path = directions_using_bfs_goal_node(goal_node)
else:
print "No solution found"
return path
def bfs_explore(problem, queue, visited):
# make first queue node as current one to explore it
current_node = queue.pop()
# check if current node is the goal node
if problem.isGoalState(current_node.get_state()):
return current_node
else:
# else add all unexplored successor nodes into the queue
# fetch all successors
successors = problem.getSuccessors(current_node.get_state())
for successor_state, successor_action, successor_cost in successors:
if successor_state not in visited:
visited.append(successor_state)
successor_node = BfsNode(successor_state, successor_action, current_node)
# todo: discuss pros and cons of commented code
# if problem.isGoalState(successor_node.get_state()):
# return successor_node
queue.push(successor_node)
return None
def directions_using_bfs_goal_node(goal_node):
directions = []
current_node = goal_node
while current_node.get_action() is not None:
directions.insert(0, current_node.get_action())
current_node = current_node.get_parent_node()
return directions
class BfsNode:
def __init__(self, state, action=None, parent_node=None):
self.state = state
self.action = action
self.parent_node = parent_node
def get_parent_node(self):
return self.parent_node
def get_state(self):
return self.state
def get_action(self):
return self.action
"""
*** Uniform Cost Search
"""
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
queue = util.PriorityQueue()
# list of positions which are already
visited = []
# goal node with it's parent node in it. Required to get path.
goal_node = None
# list of directions which lead to the goal
path = None
# add starting Node as the first node of queue and mark it visited
visited.append([problem.getStartState(), 0])
queue.push(UcsNode(problem.getStartState()), 0)
# we need to go through the entire graph
while not queue.isEmpty():
# if it finds result not none, that is our final goal, it goes out of loop
result = ucsExplore(problem, queue, visited)
if result is not None:
goal_node = result
break
if goal_node is not None:
# goal node found successfully.
path = directions_using_ucs_goal_node(goal_node)
else:
print "No solution found"
return path
def ucsExplore(problem, queue, visited):
# make first queue node as current one to explore it
current_node = queue.pop()
# check if current node is the goal node
if problem.isGoalState(current_node.get_state()):
return current_node
else:
# else add all unexplored successor nodes into the queue
# fetch all successors
successors = problem.getSuccessors(current_node.get_state())
for successor_state, successor_action, successor_cost in successors:
successor_node = UcsNode(successor_state, successor_action, current_node, successor_cost)
if is_an_acceptable_ucs_node(successor_node, visited):
visited.append([successor_state, successor_node.get_cost_till_here()])
queue.push(successor_node, successor_node.get_cost_till_here())
return None
def is_an_acceptable_ucs_node(ucs_node, visited):
"""
For ucs, in two conditions a node can be acceptable
1. It is not yet visited
2. It has lower cost than earlier visit
:param ucs_node: node with position
:param visited: list of all visited nodes
:return: true if node can be added to the queue, false otherwise
"""
for visited_state, visited_cost in visited:
if ucs_node.get_state() == visited_state:
return visited_cost > ucs_node.get_cost_till_here()
return True
def directions_using_ucs_goal_node(goal_node):
"""
ucs node contains parent node. so by tracking up until starting node is reached we can find the path
:param goal_node: final node
:return: list of directions which can lead from starting position to goal position
"""
directions = []
current_node = goal_node
while current_node.get_action() is not None:
directions.insert(0, current_node.get_action())
current_node = current_node.get_parent_node()
return directions
class UcsNode:
"""
Required node structure for UCS algo
"""
def __init__(self, state, action=None, parent_node=None, cost=0):
self.state = state
self.action = action
self.parent_node = parent_node
self.cost = cost
if parent_node is not None:
self.cost_till_here = parent_node.cost_till_here + cost
else:
self.cost_till_here = cost
def get_parent_node(self):
return self.parent_node
def get_action(self):
return self.action
def get_state(self):
return self.state
def get_cost_till_here(self):
return self.cost_till_here
"""
*** A-Star
"""
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."""
queue = util.PriorityQueue()
visited = []
goal_node = None
path = None
# add starting Node as the first node of queue and mark it as visited
starting_node = AStarNode(problem.getStartState(), None, None, 0, heuristic(problem.getStartState(), problem))
visited.append([starting_node.get_state(), starting_node.get_cost_till_here()])
queue.push(starting_node, starting_node.get_total_predicted_cost())
# look for answer until queue is empty
while not queue.isEmpty():
# if it finds result not none, that is our final goal, it goes out of loop
result = a_star_explore(problem, queue, visited, heuristic)
if result is not None:
goal_node = result
break
if goal_node is not None:
# goal node found successfully.
path = directions_using_ucs_goal_node(goal_node)
else:
print "No solution found"
return path
def a_star_explore(problem, queue, visited, heuristic):
# make first queue node as current one to explore it
current_node = queue.pop()
# check if current node is the goal node
if problem.isGoalState(current_node.get_state()):
return current_node
else:
# else add all unexplored successor nodes into the queue
# fetch all successors
successors = problem.getSuccessors(current_node.get_state())
for successor_state, successor_action, successor_cost in successors:
heuristic_value = heuristic(successor_state, problem)
successor_node = AStarNode(successor_state, successor_action,
current_node, successor_cost, heuristic_value)
if is_an_acceptable_a_star_node(successor_node, visited):
visited.append([successor_node.get_state(), successor_node.get_cost_till_here()])
queue.push(successor_node, successor_node.get_total_predicted_cost())
return None
def directions_using_astar_goal_node(goal_node):
"""
astar node contains parent node. so by tracking up until starting node is reached we can find the path
:param goal_node: final node
:return: list of directions which can lead from starting position to goal position
"""
directions = []
current_node = goal_node
while current_node.get_action() is not None:
directions.insert(0, current_node.get_action())
current_node = current_node.get_parent_node()
return directions
def is_an_acceptable_a_star_node(a_star_node, visited):
"""
For two cases, a node can be acceptable
1. It is not yet visited
2. It's cost till here is less than the recorded visit
:param a_star_node:
:param visited:
:return: true if the node is acceptable or false otherwise
"""
for visited_state, visited_cost in visited:
if a_star_node.get_state() == visited_state:
return visited_cost > a_star_node.get_cost_till_here()
return True
class AStarNode:
"""
Node structure required for A* algo
"""
def __init__(self, state, action=None, parent_node=None, cost=0, heuristic_cost=0):
self.state = state
self.action = action
self.parent_node = parent_node
self.cost = cost
if parent_node is not None:
self.cost_till_here = parent_node.cost_till_here + cost
else:
self.cost_till_here = cost
self.total_predicted_cost = self.cost_till_here + heuristic_cost
def get_parent_node(self):
return self.parent_node
def get_action(self):
return self.action
def get_state(self):
return self.state
def get_cost_till_here(self):
return self.cost_till_here
def get_total_predicted_cost(self):
return self.total_predicted_cost
# =====================================================================================================================#
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch