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 >
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