On Sat, 2007-04-07 at 01:12 +0200, Erik van der Werf wrote: > On 4/6/07, Don Dailey <[EMAIL PROTECTED]> wrote: > > However, there is nothing wrong with using alpha beta > > search with an evauation function that is not deterministic. > > I agree that some limited amount of non-determinism isn't necessarily > a bad thing, and in some cases it actually helps (e.g., when mobility > is important, or to avoid the exploitation of repeatable blunders). > However, do you really believe that this still holds if the variance > causes a spread over the maximum range of possible values of the > underlying ground-truths?
I don't understand your question. I don't claim non-determinism helps with alpha beta and I'm not recommending a fuzzy evaluation function, I'm just saying it still works. A deeper search will produce better moves in general. If you build a big tree and attach a score to all end nodes, the alpha beta mini-max procedure does not care how those nodes got their score. If the scores bear some resemblance to reality, the search will probably return a relatively good move. Even if the scores are random, it could help if the game is of the nature that maximizing your options is a good thing - which is probably most games. Some randomness may have other advantages. My theory is that in UCT, if your playouts have no randomness, the search could be locked into a deep conceptual misunderstanding that cannot be recovered from. This would be true of UCT constructed with a deterministic evaluation function. But it's only a theory of mine. With alpha beta it's tricker, randomness introduces some difficulties that reduce the efficiency of alpha beta pruning and make good move ordering more difficult. - Don > Erik _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
