Wed, 12 Dec 2007 07:14:48 -0800 (PST) terry mcintyre wrote: >Heading back to the central idea, of tuning the predicted winning rates and evaluations: it might be useful to examine lost games, look for divergence between expectations and reality, repair the predictor, and test the new predictor against a large database of such blunders.
Sounds a little like Temporal Difference Learning to me. I understand both MoGo and Crazystone use patterns, do anyone know whether they use such machine learning techniques to assign weights to them? _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
