search.py (original)
# search.py
# ———
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
“””
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 [p 85].
Your search algorithm needs to return a list of actions that reaches
the goal. Make sure to implement a graph search algorithm [Fig. 3.7].
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 ***”
util.raiseNotDefined()
def breadthFirstSearch(problem):
“Search the shallowest nodes in the search tree first. [p 81]”
“*** YOUR CODE HERE ***”
util.raiseNotDefined()
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