> This sounds like progressive widening, but it could still be progressive > unpruning, depending on implementation choices.
I do both. I have a small pool of moves that are active and I also bias the initial rave values. > > > >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? No, neither. Now I'm thinking that maybe I'm doing something different from what I thought was described in the papers. I didn't look at them carefully. I just took the name "Progressive widening", and invented something that seems to work well. > > > > 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. Agreed. That's why I always prefer to add knowledge rather than tinker with search parameters. > > 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 think it's more that Many Faces values moves that have good long-term consequences that the search can't find, so among moves with similar win rates, it will choose the ones Many Faces prefers. Or sometimes I think that UCT/MC is filling in the holes where Many Faces' knowledge is incorrect or brittle. > > 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/ _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
