>This is Aya's move predictor(W) vs GNU Go(B). >http://eidogo.com/#3BNw8ez0R >I think previous move effect is too strong.
This is a good example of why a good playout engine will not necessarily play well. The purpose of the playout policy is to *balance* errors. Following your opponent's last play is very helpful in that regard. -----Original Message----- From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Hiroshi Yamashita Sent: Friday, December 19, 2014 10:25 AM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Hi, > The predictor is white. It really does just play shapes, but > evidently it's plenty enough sometimes or against weaker opponents. I saw some games, and my impression are DCNN sees board widely. Without previous move info, DCNN can answer opponent move. It knows well corner life and death shape. It does not understand two eyes, and ladder. Tactical fight is weak. Ko fight is weak. Ko threat is simpley good pattern move. It does not understand semeai which has many libs, like 4 vs 5. So it will not help to generate semeai moves. This is Aya's move predictor(W) vs GNU Go(B). http://eidogo.com/#3BNw8ez0R I think previous move effect is too strong. Hiroshi Yamashita _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
