taking my question back. the answer is no in the context of mcts. On Tue, Nov 17, 2015 at 10:10 AM, Chun Sun <sunchu...@gmail.com> wrote:
> Is the last requirement equivalent to dynamic komi? > > On Tue, Nov 17, 2015 at 9:49 AM, Darren Cook <dar...@dcook.org> wrote: > >> > I am trying to create a database of games to do some machine-learning >> > experiments. My requirements are: >> > * that all games be played by the same strong engine on both sides, >> > * that all games be played to the bitter end (so everything on the >> board >> > is alive at the end), and >> > * that both sides play trying to maximize score, not winning >> probability. >> >> GnuGo might fit the bill, for some definition of strong. Or Many Faces, >> on the level that does not use MCTS. >> >> Sticking with MCTS, you'd have to use komi adjustments: first find two >> extreme values that give each side a win, then use a binary-search-like >> algorithm to narrow it down until you find the correct value for komi >> for that position. This will take approx 10 times longer than normal >> MCTS, for the same strength level. >> >> (I'm not sure if this is what Pachi is doing?) >> >> Darren >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > >
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