I would say you would loose too many simulations. Besides by using whatever power to increase simulations/second probably gives better results. Optimizing simulations is a dark art. I think there are several test to show thath if you make the simulation AI better the it may make your bot weaker, even with similar amount sims/move.
Perhaps applying neural nets in tree search part to bias the search? Like Many Faces does with opening book. Petri 2013/5/2 Steven Clark <[email protected]> > Thanks for the link! Looks like a good paper -- I will read it more > carefully shortly. > Ignoring computational speed for a moment, is it a reasonable assumption > that an algorithm that plays a NN-proposed tactical move 50% of the time, > and a random move 50% of the time, should outperform an algorithm that > plays a random move 100% of the time? > So it's just a case of how many playouts do we lose by employing the NN > (GPUs to the rescue?). For reference, I was using 25 input nodes, 25 output > nodes, ~50 hidden nodes. > I guess ultimately it comes down to "make a bot and prove it" :) > > -Steven > > > > On Wed, May 1, 2013 at 9:50 PM, George Dahl <[email protected]> wrote: > >> I don't know if neural nets that predict moves have been helpful in any >> strong bots, but predicting expert moves with neural nets is certainly old >> news. See http://www.cs.utoronto.ca/~ilya/pubs/2008/go_paper.pdf >> >> There might be a place for artificial neural nets in a strong Go playing >> program, but it is an open question on how to incorporate neural nets well. >> Software like neurgo used a lot of expert features along with a neural net >> for global position evaluation and I tried (with very little success) to >> predict ownership of points on the board using a neural net. >> >> It is very hard to get neural nets to help a standard MCTS bot a lot >> because the neural net needs to be good at whatever it is supposed to be >> doing and still probably very fast to be useful. >> >> - George >> >> >> On Wed, May 1, 2013 at 9:42 PM, Steven Clark <[email protected]>wrote: >> >>> Hello all- >>> >>> Has anyone successfully used neural nets to help guide MC playouts? >>> Has anyone used NN to learn patterns larger than 3x3? >>> >>> I'm working on a grad-school project, and discovered a few interesting >>> things. >>> After analyzing 10,000+ high-dan games from KGS, I find that more than >>> 50% of the time, moves are played within a 5x5 window centered at the >>> opponent's previous move (call this a "tactical" move, vs a strategic move). >>> >>> I used the FANN library to learn these 5x5 patterns, and found that the >>> NN could predict tactical moves with ~27% accuracy (and with a >50% chance >>> that the answer would be in the top 3 moves proposed by the NN). >>> >>> Is this old news? Are neural nets just too slow to be helpful to MC >>> (reduce the playout rate too much?) >>> >>> Thoughts welcome. I will be up late finishing the report since it is due >>> tomorrow ;) >>> >>> -Steven >>> >>> _______________________________________________ >>> 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 >
_______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
