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in AI and Deep Learning by (50.2k points)

I'm looking for the best algorithm to optimize the decisions made in a simulation to find a fast result in a reasonable amount of time. The simulation does several "ticks" and occasionally needs to make a decision. Eventually, a goal state is reached. ( It would be possible to never reach a goal state if you make very bad decisions )

There are many many goal states. I want to find the goal state with the least number of ticks ( a tick equates roughly to a second in real life." I want to decide which decisions to make to get to the goal in as few seconds as possible,

Some points about the problem domain:

  • Straight off the bat, I can generate a series of choices that will lead to a solution. It won't be optimal.

  • I have a reasonable heuristic function to determine what would be a good decision

  • I have a reasonable function to determine the minimum possible time cost from a node to a goal.


  • I need to process this problem for about 10 seconds and then give the best answer I can.

  • I believe A* would find me the optimal solution. The problem is that the decision tree will be so large that I won't be able to calculate it quick enough.

  • IDA* would give me a good first few choices in 10 seconds but I need a path to a goal.

At the moment I am thinking that I will start with the known non-optimal path to a goal and then perhaps use Simulated Annealing and attempt to improve it over 10 seconds.

What would be a good algorithm to research to try to solve this sort of problem?

1 Answer

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by (108k points)

If you have a good heuristic you should be able to utilize it to compare individual choices - for the limited discrepancy search and compare partial solutions, for the beam search.

Have a look at limited discrepancy search, repeating with increasingly loose limits on the maximum discrepancy search, or beam search.

If you can set an upper bound on how good any extension of an incomplete solution is. Then you can clip out the incomplete solutions that can't possibly be extended to beat the result from the heuristic, or the best result found so far in a series of iterative searches with increasing depth.

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