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in AI and Deep Learning by (50.2k points)

I have a maze-like the following:


The goal is for P to find +, with sub-goals of

  • The path to + is the least cost (1 hop = cost+1)

  • The number of cells searched (nodes expanded) is minimized

I'm trying to understand why my A* heuristic is performing so much worse than an implementation I have for Greedy Best First. Here are the two bits of code for each:

 #Greedy Best First -- Manhattan Distance

self.heuristic = abs(goalNodeXY[1] - self.xy[1]) + abs(goalNodeXY[0] - self.xy[0])

#A* -- Manhattan Distance + Path Cost from 'startNode' to 'currentNode'

return abs(goalNodeXY[1] - self.xy[1]) + abs(goalNodeXY[0] - self.xy[0]) + self.costFromStart

In both algorithms, I'm using a heapq, prioritizing based on the heuristic value. The primary search loop is the same for both:

 theFrontier = []

heapq.heappush(theFrontier, (stateNode.heuristic, stateNode)) #populate frontier with 'start copy' as only available Node

#while !goal and frontier !empty

while not GOAL_STATE and theFrontier:

    stateNode = heapq.heappop(theFrontier)[1] #heappop returns tuple of (weighted-idx, data)


    while stateNode.moves and not GOAL_STATE:

        EXPANDED_NODES += 1

        moveDirection = heapq.heappop(stateNode.moves)[1]

        nextNode = Node()


        #this makes a call to setHeuristic

        nextNode.setLocation((stateNode.xy[0] + moveDirection[0], stateNode.xy[1] + moveDirection[1]))

        if nextNode.xy not in CHECKED_NODES and not isInFrontier(nextNode):

            if nextNode.checkGoal(): break


            heapq.heappush(theFrontier, (nextNode.heuristic,nextNode))

So now we come to the issue. While A* finds the optimal path, it's pretty expensive at doing so. To find the optimal path of cost:68, it expands (navigates and searches through) 452 nodes to do so.

To find the optimal path of cost:68, it expands (navigates and searches through) 452 nodes to do so.


While the Greedy Best implementation I have finds a sub-optimal path (cost: 74) in only 160 expansions.


I'm really trying to understand where I'm going wrong here. I realize that Greedy Best First algorithms can behave like this naturally, but the gap in node expansions is just so large I feel like something has to be wrong here... any help would be appreciated. I'm happy to add details if what I've posted above is unclear in some way.

1 Answer

0 votes
by (108k points)

A* produces the optimal answer to the problem, greedy best-first search provides any solution.

It's supposed that A* has to perform more work.

You can take a look at weighted A* if you want a variation of A* that is not optimal anymore but returns a solution much faster. It just consists of putting weight to the heuristic 

(weight > 1). In practice, it gives you an immense performance increase

For example, could you try this :

syntax: return 2*(abs(goalNodeXY[1] - self.xy[1]) + abs(goalNodeXY[0] - self.xy[0])) + self.costFromStart

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