> 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. OK, by i used "bias" in common sense, to mean that the "strong simulator" has preferences for some moves, and doesn't consider them equally, or worse doesn't consider some moves.
Ok you are talking about "bias" on the moves, I was talking about bias on the Monte-Carlo simulations outcomes (difference between the expectation of the random variable and the real value you want to estimate). So you are more talking about difference with the uniform distribution on moves. I think what we care about is the MC outcomes. Particular moves played by the simulation player do not matter.
So it will miss some good points due to its knowledge, whereas the random player will find the move.
But we don't care about the random player "finding" or not the move. If the random player plays with probability 1/100 the good move, and also does not find the good answers afterwards, it is not clear how it changes the expectation of the outcomes.
> > 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 m 100% sure of this :-)
May I be 99% sure that you should not be 100% sure of this? ;-) I think that without having empirical evidences, we can't be 100% 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). Even with randomization, GNU Go considers only a few dozen of possible moves, and makes systematic errors.
You can be epsilon greedy if you want to avoid systematic errors.
Some times ago Rémi Coulom asked for "positions illustrating computer stupidity" (2006-11-22) http://computer-go.org/pipermail/computer-go/2006-November/007107.html and GNU Go provided some nice examples where its (wrong/misunderstood) >knowledge induces a failure in play.
I bet we can find much more positions where uniform random would give the wrong answer with high probabilty, isn't it? Furthermore, we are not talking about having a perfect player. There will always have particular positions where computer sucks, as I am sure we can find positions where human sucks.
> I understand all these counter examples, I just think that it is more > complicated than that. I fully agree.
Good, we only have to find how to do better then ;-). Sylvain _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
