Is the last requirement equivalent to dynamic komi?

On Tue, Nov 17, 2015 at 9:49 AM, Darren Cook <> 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
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