On Wed, Oct 14, 2009 at 03:34:59PM +0300, Petri Pitkanen wrote: > Neural network tend to work well in those cases where evaluation function is > smooth, like backgammon. Even inbackgammon neural networks do give good > results if situation has possibility of sudden equity changes like deep > backgames and deep anchor games. Top backgammon programs 3-ply search on top > neural network to handle these problems. > > I do not know wher neural nets would fit well, perhaps finding invasion > spots?
I have been speculating about a NN evaluation function for go, feeding it a lot of preprocessed information about the position, like number of strings with 1,2,3,4, or more liberties, number of stones in same, number of separate groups, number of "obviously" dead stones, strings, and groups, number of points "clearly" controlled by each player, etc, etc. This should be possible to train from existing games where we know the result (in the beginning it is 50-50, in the end one or the other has won 100-0. Assume some simple function in between). I have never had the time nor the patience to pursue this any further - I have more interesting ideas, and far too little time... If anyone wants to play with it, I'd love to hear of any results. - Heikki -- Heikki Levanto "In Murphy We Turst" heikki (at) lsd (dot) dk _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
