> 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?)


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