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search.py
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search.py
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# search.py
# ---------
# 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).
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).
"""
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 master_search_hyper(fringe, problem):
# %init
parent = {}
visited = []
path = []
start_state = problem.getStartState()
state = ((start_state, 'Start', 0), 'Root')
fringe.push(state)
while not fringe.isEmpty():
# %pop
u = fringe.pop()
current_state = u[0][0]
action = u[0][1]
step_cost = u[0][2]
parent_state = u[1]
# uses the visited array, to ignore the already visited nodes(states)
if current_state in visited:
continue
# The parent dictionary has the below configuration.
# KEY -> current_state
# VALUE -> (parent_state, action_from_parent_to_current)
parent[current_state] = (parent_state, action)
# Visited Array holds the states expanded
visited.append(current_state)
# %Goal Test
if problem.isGoalState(current_state):
# uses the parent dictionary to re-assemble the path from the goal,
# current_state to the source.
path = reconstruct_path(parent, current_state)
break
for neighbour in problem.getSuccessors(current_state):
# %parseNeighbour
neigh_state = neighbour[0]
neigh_action = neighbour[1]
# cumulative_cost = cost_from_parent + step_cost_to_neighbour
neigh_cost = neighbour[2] + step_cost
# creates the neighbour_state and pushes onto the fringe.
new_state = ((neigh_state, neigh_action, neigh_cost), current_state)
fringe.push(new_state)
return path
def reconstruct_path(parent, goal_state):
list_of_moves = []
# Till the start(root) state is not reached, keep appending the actions.
# parent[state] => (parent_state, action_from_parent_to_child)
while parent[goal_state][0] != 'Root':
parent_state = parent[goal_state][0]
action = parent[goal_state][1]
list_of_moves.append(action)
goal_state = parent_state
# we get the actions from goal to root, now reverse for directions from
# root to goal.
list_of_moves.reverse()
return list_of_moves
def depthFirstSearch(problem):
"""
Search the deepest nodes in the search tree first.
"""
from util import Stack
stk = Stack()
return master_search_hyper(stk, problem)
def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
from util import Queue
queue = Queue()
return master_search_hyper(queue, problem)
def uniformCostSearch(problem):
"""Search the node of least total cost first."""
from util import PriorityQueueWithFunction
# the step cost is the actual cumulative cost from parent to state,
# as calculated by the master_search algorithm.
cumulative_cost = lambda x : x[0][2]
pqueue = PriorityQueueWithFunction(cumulative_cost)
return master_search_hyper(pqueue, problem)
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."""
from util import PriorityQueueWithFunction
# total_cost = cumulative cost from parent to current node +
# heuristic cost from current node to goal.
# in other words, f(n) = g(n) + h(n)
total_cost = lambda x : x[0][2] + heuristic(x[0][0], problem)
pqueue = PriorityQueueWithFunction(total_cost)
return master_search_hyper(pqueue, problem)
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch