[Computer-go] CGOS source on github

David Wu lightvector at gmail.com
Fri Jan 22 06:26:23 PST 2021


Hi Claude - no, generally feeding liberty counts to neural networks doesn't
help as much as one would hope with ladders and sekis and large capturing
races.

The thing that is hard about ladders has nothing to do with liberties - a
trained net is perfectly capable of recognizing the atari, this is
extremely easy. The hard part is predicting if the ladder will work without
playing it out, because whether it works depends extremely sensitively on
the exact position of stones all the way on the other side of the board. A
net that fails to predict this well might prematurely reject a working
ladder (which is very hard for the search to correct), or be highly
overoptimistic about a nonworking ladder (which takes the search thousands
of playouts to correct in every single branch of the tree that it happens
in).

For large sekis and capturing races, liberties usually don't help as much
as you would think. This is because approach liberties, ko liberties, big
eye liberties, shared liberties versus unshared liberties, throwin
possibilities all affect the "effective" liberty count significantly. Also
very commonly you have bamboo joints, simple diagonal or hanging
connections and other shapes where the whole group is not physically
connected, also making the raw liberty count not so useful. The neural net
still ultimately has to scan over the entire group anyways, computing these
things.

On Fri, Jan 22, 2021 at 8:31 AM Claude Brisson via Computer-go <
computer-go at computer-go.org> wrote:

> Hi. Maybe it's a newbie question, but since the ladders are part of the
> well defined topology of the goban (as well as the number of current
> liberties of each chain of stone), can't feeding those values to the
> networks (from the very start of the self teaching course) help with large
> shichos and sekis?
>
> Regards,
>
>   Claude
> On 21-01-22 13 h 59, Rémi Coulom wrote:
>
> Hi David,
>
> You are right that non-determinism and bot blind spots are a source of
> problems with Elo ratings. I add randomness to the openings, but it is
> still difficult to avoid repeating some patterns. I have just noticed that
> the two wins of CrazyStone-81-15po against LZ_286_e6e2_p400 were caused by
> very similar ladders in the opening:
> http://www.yss-aya.com/cgos/viewer.cgi?19x19/SGF/2021/01/21/733333.sgf
> http://www.yss-aya.com/cgos/viewer.cgi?19x19/SGF/2021/01/21/733301.sgf
> Such a huge blind spot in such a strong engine is likely to cause rating
> compression.
>
> Rémi
>
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