> > I read a paper a couple years ago about a genetic algorithm to evolve > a neural network for Go playing (SANE I think it was called?). The > network would output a value from 0 to 1 for each board location, and > the location that had the highest output value was played as the next > move. I had an idea that the outputs could be sorted to get the X > "best" moves, and that that set of moves could be used to direct a > minimax or monte carlo search. I haven't had the chance to prototype > this, but I think it would be an interesting and possibly effective > way to combine neural networks with the current Go algorithms. > > Colin
This was a great achievement indeed, but although it might seem dumb, my approach here is to be as ignorant as I can (not very difficult given my knowledge in AI) of subtile and clever ways to make my players evolve. The SANE algorithm has proven to be very powerful, but it needs some assumptions to be true. As "probably true" this assumtpions are, I prefer to have none and look at a really random evolution pattern. Ernest.
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