Author: Jay Scott
Date: 13:10:13 04/27/05
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On April 27, 2005 at 10:35:28, Michael Yee wrote: >- learn rules/functions for when to extend or reduce search along a given line This would be a good area. Most of the attempts so far are in the line of general algorithms, rather than attempts to automatically find features of the situation which indicate that search reduction is likely to be a good idea. Also, good search reduction can be a huge win, and I believe there's some potential to come up with a breakthrough idea. >- learn when it's safe to prune a given line (related to previous idea) That means "reduce search to nothing", so it's a special case of the last one. >- learn parameters for static (leaf node) evaluation function (although a lot >has already been done here, I think) This is the most popular thing to try, because it's easy to play around with. There have been moderate successes. A success would be an automatically-tuned evaluator, or even evaluator component, which was competitive with a hand-tuned one and takes less work to retune when adding new parameters. A bigger success would be to convince conservative chess programmers that this was true, so that they used the method! >- learn/construct/discover features for a static evaluation function (some >not-so successful work may have been done here with neural networks?) This is wide open. There's no previous work that I consider successful. I believe it's easy in principle; it's only hard in practice. :-) But it does depend on getting the previous step to work, tuning, so it's not the thing to try first. You have to be able to tune your constructed features so you can know if they're any good--deserving of a high weight in the evaluator, rather than a near-zero weight. >- learn rules for move ordering (i.e., that try to search best moves from a >given node first to achieve more efficient cut-offs) Unlikely to be a big win, because existing heuristics are highly successful. >Specifically, I'm curious which areas are already "mature", which seem >promising/new, or even if you have any other ideas/references. In my opinion, no area of machine learning in chess is mature. Some machine learning techniques are used in production chess programs, but they are very limited. The most common use is learning opening books, which commonly work by rote learning and ad hoc score adjustments. I'd also note that search control and position scoring interact. Each has to be tuned in the context of the other. If you're just starting out, it'll be easier to work on one or the other and not both. Jay
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