Stefan Kaitschick wrote: > This is really a quirk of the go ranking system, which > defines strength as the ability to give handicap stones. > If strength were defined as being able to win a certain > percentage of even games, things would be different.
That is a very important issue. Assuming 1 stone = xx Elo points is wrong and becomes even worse as the level rises. Another issue we may have not taken seriously enough is intransitivity. We say it is not very important, and that may be true for humans, but nothing like a computer to force "non important" things to become most important. When I was experimenting with learning playout weights using GAs (something abandoned I have something much better in progress) I found easy to make a chain where: B is 50 Elo point stronger than A C is 50 Elo point stronger than B, D than C, E than D And when you confront E vs A and expect the difference to to be 200-ish it is only 30 points. And all this is done with appropriate Agresti-Coull confidence intervals and significance tests for the difference, so it is not a conclusion based on a wrong setup. I bring this here because it is hardly conceivable that a program does not scale in self play, something must be broken. But I can see Hideki's point that this scaling may take the program nowhere when it is about advancing quantum leaps. The infinite convergence of the tree is a beautiful argument but of no practical application. The tree simply does not grow in directions the playouts discourage because they don't understand the precise sequences. If we do another study, one big difficulty will be finding strong and different (= non MCTS) opponents to make the pool a little less homogeneous. Else, the study may be too much influenced by Elo rating limitations. Jacques. _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
