[Computer-go] Need help with fuego source code

René van de Veerdonk rene.vandeveerdonk at gmail.com
Mon Jun 24 10:38:36 PDT 2013

Looking at it from a distance, this sounds like a fancy way of saying that
you created an opening book. This may sound a little strange and a
mis-characterization of your effort, but please entertain the thought for a
while. What you are attempting to do is to bias the move selection in the
opening phase using priors on the top-30 moves. Perhaps Fuego's opening
book code would allow you to import your weights outside of the
tree/playout code. Now, typically opening books contains well-defined lines
of play, whereas yours would be a model, so integration may not be that
straightforward. You would also lose the guidance inside the random


PS. Welcome to the list.

On Mon, Jun 24, 2013 at 8:33 AM, David Briemann <dbriemann at gmail.com> wrote:

> Well it is an attempt to improve the playing strength, but that won't mean
> that it succeeds.
> What I do is the following(in short):
> I have a trained move predictor model which consumes a board situation and
> outputs beliefs for every playable move.
> I want to use it to bias the search tree for the first N moves of a game
> (opening phase).
> So when tree search generates all legal moves, the predictor will score
> them and only consider the best X move as legal moves.
> It then should be forced to play "good" opening moves(of couse only if the
> predictions make sense).
> David
> 2013/6/24 Don Dailey <dailey.don at gmail.com>
>> On Mon, Jun 24, 2013 at 7:58 AM, David Briemann <dbriemann at gmail.com>wrote:
>>> I'm beginning to think that I didn't understand the tree search part
>>> correctly. You say the tree search generates moves too. I thought moves
>>> were only generated in playouts and the tree search part was to follow
>>> already played lines until it reaches a position which has not been played
>>> out. Probably that's the location were I have too look into.
>> I don't know the gory details of the implementation,  but clearly the
>> tree portion of the search considers all moves (sooner or later) and much
>> has been written about how MCTS is admissible - at least in theory.    That
>> means it would,  if given enough time and memory,  play perfect go and will
>> consider every legal move at some point.    But we know that playouts are
>> not fully random and in many positions will only play a limited number of
>> moves (perhaps just one) such as when defending atari.     So the search
>> tree portion is not constrained by only what the next playout move will
>> return.
>> Read the code - and perhaps any documentation that comes with this
>> program.   One this is clear to me though,  if you impose patterns
>> non-probabilistically on the tree you will weaken the program considerably.
>>     The reason MCTS works so incredibly well is that we have put patterns
>> in their proper place,  as move guidance and not as a plausible move
>> generator only.     The old style weak programs were heavily pattern based.
>>     So I may be misunderstanding what you are trying to do - is this a
>> study of some kind or a real attempt to improve the program?
>> Don
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