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.
I think we have to start defining what the bias. For me the bias is the difference between the expected value of the outcomes of playouts by the simulation player and the "real minimax value". In this definition the uniform random simulation player is VERY biased and gnugo much less.
A trivial example is GNU Go: its analyze is "sometimes" wrong.
Of course, if not computer go would be solved :-).
Even if it is obviously much stronger than a random player, it would give wrong >result if used as a simulation player.
Hum, are you sure? I think that GnuGo with randomisation, (and much faster of course) would make a very good simulation player (much better than any existing simulation player). But a weaker player than GnuGo can make an even better 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.
Yes, this a similar non trivial result. I think there are more existing experimental and theoritical analysis of this, though. Perhaps such an analysis already exist for MC also, it is just that I don't know.
Or maybe i just understood nothing of what you explained ;)
It was not really "explanations", just thoughts. I have no the solution, just think that it is an interesting question, and that it may be discussed. May be from a strong explanation of this phenomenon could come new ideas. I understand all these counter examples, I just think that it is more complicated than that. Sylvain _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
