Author: Jay Scott
Date: 07:38:50 08/21/98
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On August 20, 1998 at 12:23:33, Torsten Schoop wrote: >does anyone know why NN backgammon programs play very well (TD-Gammon, >JellyFish, Snowie)but the NN chess playing programs do not? No, nobody knows. Or if they do they haven't convinced everybody else. :-) My favorite theory is inspired by the analysis of what kind of backgammon positions the neural net programs are less good at playing. They're what backgammon players call "technical positions", where the choice of best move depends on calculation of the probabilities of future events. In a typical backgammon position, the dice rolls "smear out" what chess players would think of as the tactical possibilities of the position, and the neural network's estimate of the relative probabilities of events is excellent. In a technical position, which is comparatively rare, expert humans can calculate the probabilities closely, but the neural network still uses its everyday estimate, which is not as accurate. Now compare: in chess, most positions are "technical" positions that depend on exact calculation! So you shouldn't expect a network's estimate to be particularly accurate. I have a brief discussion of it on my Machine Learning in Games site, on this page: http://forum.swarthmore.edu/~jay/learn-game/systems/gammon.html Some other theories are, briefly: - Chess is more complicated. - Not as much work has gone into good neural network chess evaluation. - We don't happen to have discovered "good" neural network input features for chess; they might be different from good traditional evaluation features. - We haven't figured out the right way to train a net to produce the most useful evaluation function. Jay
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