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 >
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