> I start with one move, and slowly add moves to the > pool of moves This sounds like progressive widening, but it could still be progressive unpruning, depending on implementation choices.
Progressive unpruning is the technique that assigns initial biases to RAVE values. The point is that as real RAVE observations take place, some moves that previously held low interest will be tried for the first time (that is, "unpruned"). >My current schedule looks like: To be sure that I understand, MF orders the moves using static analysis, and then the ordering is further modified by RAVE observations? So when Many Faces accumulates Schedule(N) trials, it will restrict its attention to the N highest ranked moves according to the combined Static + UCT/RAVE? Or does MF restrict the choice to the highest N by Static eval, and then order the top N by UCT/RAVE? > if you are just using RAVE to do move ordering you might > need to widen faster. I recall that you credited the use of Many Faces rules with a massive improvement against GnuGo, so the technique is certainly empirically justified. But I am wondering how it achieves its results. That is, what do you think the difference is, compared to standard unpruning? There is a rule that I live by, which is "GG >> SS". This rule (really a universal law, when you get right down to it) states that a Good Guess is much better than a Short Search. So is the benefit that MF avoids wasting trials on moves that were just lucky in early trials, but probably will not stand up? I am also wondering whether you could achieve the same effect by using pure progressive unpruning, but with a heavier weight (e.g., 100 trials) for Many Faces opinion. _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
