On Wed, Apr 8, 2009 at 14:41, Jason House <jason.james.ho...@gmail.com> wrote: > On Apr 8, 2009, at 3:15 AM, Łukasz Lew <lukasz....@gmail.com> wrote: > >> On Tue, Apr 7, 2009 at 23:52, Claus Reinke <claus.rei...@talk21.com> >> wrote: >>> >>> Last time I looked more closely at what my MC bot (simple, no tree) >>> was doing, I noticed that it has a tendency to try the impossible moves >>> (suicides) first, discarding them as impossible for every genmove. >>> >>> Looking at more statistics, and thinking about it, it finally dawned on >>> me that this is a consequence of the standard move evaluation approach >>> based on win rate: >>> >>> - what one hopes for is the move with the best chance of winning >>> (move enables win) >>> - what one might get is a move that can only be played when winning >>> (win enables move) >>> >>> For suicide moves, this is particularly obvious: they become possible >>> only in the small number of games in which the opponent group >>> surrounding the pseudo-eye has died (or is killed by playing in the >>> pseudo-eye after removing all other liberties). The larger the group, >>> the more likely that the game is going to be won if that group dies >>> (roughly), so the larger the opponent group, the more tempting its >>> pseudo-eyes seem for win-rate-based evaluation, however unlikely >>> it is that the group actually dies in non-random play (certainly not >>> by starting with the pseudo-eye). >>> >>> Something similar might be happening at a less obvious scale, >>> such as playing a move into self-atari: there is one opponent >>> move that renders this useless, but it is only one move - any >>> game in which the stone is not captured might look rather >>> good in terms of winning. >> >> Good insight, well known too. >> >>> >>> Is there a known method of reducing the impact of these outliers >>> (other than building a real tree and seeing by trial-and-error >>> that many good-looking moves aren't really useful)? >> >> Heavy playouts introduce lot's of knowledge to avoid moves that can be >> easily and with high probability detected that are bad. >> >> If you are asking about domain-independent techniques, then only MCTS >> and AMAF/RAVE are well known. >> Nothing else is in popular. But more and more people are thinking how >> to make adaptive playouts. > > Heavy playouts aren't the only way. Initialization heuristics and > progressive widening also work.
Indeed :) > > >>> It seems >>> that one cannot just devalue moves with low hit counts - after >>> all, if there is only one sequence of moves that will rescue the >>> game, one doesn't want to discount it just because it is rare. >>> >>> One thing that might help are move-number statistics: those >>> moves that tend to be played late in the playouts in which >>> they occur might depend on other moves to be played first, >>> so perhaps one should have lower bounds on when each >>> move can be considered for actual play? >> >> In light playout moves are played at random, so the moment of playing >> a move doesn't carry too much information. >> >> Lukasz >> >>> >>> Claus >>> >>> >>> >>> >>> _______________________________________________ >>> 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/ > > _______________________________________________ > 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/
