Nice work. Thanks for sharing. It would be nice if you could reduce the error margins in the even games against gnugo, to know for sure whether there is an advantage to be had there.
On Mon, Oct 4, 2010 at 5:31 PM, Petr Baudis <[email protected]> wrote: > Abstract: > Monte Carlo Tree Search of the game of Go tends to produce unstable and > unreasonable results when used in situations of extreme advantage or > disadvantage, due to poor move selection because of low signal-to-noise > ratio; notably, this occurs when playing in high handicap games, > burdening the computer with further disadvantage against the strong > human opponent. We explore and compare multiple approaches to mitigate > this problem by artificially evening out the game based on modification > of the final game score by variable amount of points (``dynamic komi'') > before noting the result in the game tree. We also compare performance > of MCTS and traditional tree search in the context of extreme situations > and measure the effect of dynamic komi on actual playing strength of a > state-of-art MCTS Go program. Based on our results, we also conjencture > on resilience of the game search tree to changes in the evaluation > function throughout the search. > > It is still few days away from being ready for submission, but I figured > that given the recent surge of interest in dynamic komi at the list, it > might be useful to make an earlier preprint version available: > > http://pasky.or.cz/~pasky/go/dynkomi.pdf > > Any comments and suggestions are welcome! > > -- > Petr "Pasky" Baudis > The true meaning of life is to plant a tree under whose shade > you will never sit. > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go > _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
