Hi Rene, It may have sounded like you describe it but I think it's still a bit different. I don't think opening books go deep into the game. For example if I say the top N moves right now that means N=100. But that could change to 150 or more depending on the quality of the predictor. Also opening book lookups don't consider the whole board but just local situations as far as i know.
But I will remember what you said and if I still have time for it I will look into the Fuego's opening book code. David 2013/6/24 René van de Veerdonk <[email protected]> > 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 > playouts. > > Rene > > PS. Welcome to the list. > > > On Mon, Jun 24, 2013 at 8:33 AM, David Briemann <[email protected]>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 <[email protected]> >> >>> >>> On Mon, Jun 24, 2013 at 7:58 AM, David Briemann <[email protected]>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 >>> >>> >>> _______________________________________________ >>> Computer-go mailing list >>> [email protected] >>> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >>> >> >> >> _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >> > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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