Le jeudi 8 février 2007 20:12, Sylvain Gelly a écrit : > > One simple explaination could be that a random player shamelessly tries > > "all" > > moves (very bad ones but also very nice tesuji) whereas the "stronger" > > player > > is restricted by its knowledge and will always miss some kind of moves. > > Here we are not speeking about the pruning in the tree, but the > simulation player. The tree must explore every move, to avoid missing > important ones. However we totally don't care if all possible games > can or not be played by the simulation player. What we care about is > the expectation of the wins by self play. > If the simulation player sometimes play meaningful sequences but with > a very small probability, then it has very little influence on the > expectation. >
It seems i was ambiguous: I was speaking of the simulation player too. What i meant is a random simulation player is not biased, whereas a "better" simulation player is biased by its knowledge, and thus can give wrong evaluation of a position. A trivial example is GNU Go: its analyze is "sometimes" wrong. Even if it is obviously much stronger than a random player, it would give wrong result if used as a simulation player. David Doshay experiments with SlugGo showed that searching very deep/wide does not improve a lot the strength of the engine, which is bound by the underlying weaknesses of GNU Go. Or maybe i just understood nothing of what you explained ;) Alain _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
