Archived
165 lines
5.5 KiB
Python
165 lines
5.5 KiB
Python
# 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
|
|
# user made functions
|
|
|
|
|
|
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 baseSearchFunction(problem, data_structure):
|
|
"""
|
|
Since the implementation of a Depth First Search and Breadth First Search
|
|
are nearly identical, we can simply switch the date structure based on the
|
|
order we recieve our nodes; i.e. change the direction we pop, push nodes.
|
|
"""
|
|
# The path that takes us from the start node to the destination
|
|
answer_path = []
|
|
|
|
# Set the open_nodes data structure as either a Stack (DFS) or Queue (BFS)
|
|
open_nodes = data_structure()
|
|
|
|
# Add the starting state and the empty list to our open nodes
|
|
open_nodes.push((problem.getStartState(), answer_path))
|
|
|
|
# Initialize closed nodes to contain nothing
|
|
closed_nodes = []
|
|
|
|
# While there is at least one item in our open_nodes
|
|
while(not open_nodes.isEmpty()):
|
|
|
|
# Assign current_node and answer_path to an element from one side of the list
|
|
current_node, answer_path = open_nodes.pop()
|
|
|
|
# If our current node isn't closed
|
|
if(current_node not in closed_nodes):
|
|
|
|
# Add the current node to the set of closed nodes
|
|
closed_nodes.append(current_node)
|
|
|
|
# If we're at the destination
|
|
if(problem.isGoalState(current_node)):
|
|
# Return the path we used to get here
|
|
return answer_path
|
|
|
|
# Set successors equal to the children of the current node
|
|
successors = problem.getSuccessors(current_node)
|
|
|
|
# For each successor
|
|
for state in successors:
|
|
|
|
#Set the node, path equal to the node and path of the state (We're not concerned about the cost)
|
|
node, path, _ = state
|
|
|
|
#If the current node isn't in the set of closed nodes
|
|
if(node not in closed_nodes):
|
|
|
|
# Add the node and path to the set of open nodes
|
|
open_nodes.push((node, answer_path + [path]))
|
|
|
|
#Return the 'best' answer_path we could find; If we're returning here, we didn't find the destination
|
|
return answer_path
|
|
|
|
def depthFirstSearch(problem):
|
|
"""Search the deepest nodes in the search tree first."""
|
|
|
|
#Use a stack in order to perform a Depth First Search
|
|
return baseSearchFunction(problem, util.Stack)
|
|
|
|
def breadthFirstSearch(problem):
|
|
"""Search the shallowest nodes in the search tree first."""
|
|
|
|
#Use a Queue in order to perform a Breadth First Search
|
|
return baseSearchFunction(problem, util.Queue)
|
|
|
|
def uniformCostSearch(problem):
|
|
"""Search the node of least total cost first."""
|
|
"*** YOUR CODE HERE ***"
|
|
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 ***"
|
|
util.raiseNotDefined()
|
|
|
|
|
|
# Abbreviations
|
|
bfs = breadthFirstSearch
|
|
dfs = depthFirstSearch
|
|
astar = aStarSearch
|
|
ucs = uniformCostSearch
|