Thanks for your answer.

Unfortunately Pachi doesn't seem to really try to maximize score, even with
these settings: Once one side has won by a large enough margin, it will
stop trying to kill small groups and I am precisely trying to generate a
database to learn about life and death. Perhaps I can play around with the
settings in uct/dynkomi.c to see if I can make it behave closer to what I

If anyone knows of a program that can actually use something like expected
score in the UCT move selection formula, that's probably what I need. I
might end up modifying Pachi to do this, but it sounds daunting.


On Tue, Nov 17, 2015 at 5:26 AM, Josef Moudrik <> 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.
>> This last requirement is a bit unusual. Ideally I would like to do this
>> on 9x9 first (to be able to try many things quickly) and then 19x19 (the
>> real thing).
>> Is there a strong engine that would allow me to do this? Linux or Mac
>> strongly preferred. I'll be happy to pay for it if it's commercial.
> Hello,
> pachi ( ) is reasonably strong and you can achieve what you
> want by using following comandline options:
> pass_all_alive,maximize_score,resign_threshold=0.0
> but as the README for maximize_score says:
> Note that Pachi in this mode may be slightly weaker, and result margin
> should not be taken into account when judging either player's strength.
> During the game, the winning/losing margin can be approximated from
> Pachi's "extra komi" or "xkomi" reporting in the progress messages.
> Regards,
> Josef
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