知识有限且无距离启发式的寻路

Question

I'm having trouble writing the pathfinding routine for the AI in a simple Elite-esque game I'm writing.

The world in this game is a handful of "systems" connected by "wormholes", and a ship can jump from the system it's in to any system it's linked to once per turn. The AI is limited to only knowing things that it should know; it doesn't know what links come from a system it hasn't been to (though it can work it out from the systems it has seen, since links are two-way). Other parts of the AI decide which system the ship needs to get to based on what goods it has in its inventory and how much it remembers things being worth on systems it has passed through.

The problem is, I don't know how to approach the problem of finding a path to the target system. I can't use A*; there's no way to determine the 'distance' to another system without pathing to it. I also need this algorithm to be efficient, since it'll need to run about 100 times every time the player takes his turn.

Does anyone know of a suitable algorithm?

Answer 1

I ended up implementing a bidirectional, greedy version of breadth-first search, which suits the purpose well enough. To put it simply, I just had the program look through each node its starting node connected through, then each node those nodes connected to, then each node those connected to... until the destination node was found.

Normally one would build a list of appropriate paths and pick the shortest one, but I tried a different method; I had the program run two searches in parallel, one from the starting point, and one from the end point. When the 'from' search found the last node of the 'to' search, the path was considered found.

It then optimizes the path by checking if each node on the path connects to a node further up in the path, and deleting each node in between them.

Whether or not this funky algorithm is actually any better than a straight BFS remains to be seen.

Answer 2

When it comess to unknown environments, I usually use an evolutionary algorithm approach. It doesn't guarantee that you'll find the best solution in the small timeframe you have, but is a way to approach such a problem.

Answer 3

Have a look at Partially Observable Markov Decision Problems (POMDP). You should be able to express your problem with this model.

Then you can use an algorithm that solves these problems to try to find a good solution. Note that solving POMDPs is usually very expensive and you probably have to resort to approximate methods.

Answer 4

Easiest way to cheat this would be o go through, or at least attempt to access as many systems as possible, then implement the distance heuristic as the sum of all the systems you've been to.

Alternatively, and way cooler:

I've implemented something similar using ACO (Ant colony optimization) and worked pretty well combined with PSO(particle swarm optimization), however, the additional constraints your system is imposing means that you'll have to spend a few (at least one) sessions figuring out the environment layout, and if it's dynamic... well... though.
The good thing is that this algorithm completely bypasses the need for heuristic generation which is what you need since you are flying blind. Be advised though, that this is a bad idea if your search space (number of runs) is small. (100 may be acceptable, but 10 or 5 ... not so much).

This scales up quite nicely when dealing with large numbers of nodes (systems) and it bypasses the heuristic distance computational need for every node-to-node relationship, thereby making it more efficient.

Good luck.