> Datum: Fri, 17 Jun 2011 18:22:32 -0400 > Von: Michael Williams <[email protected]> > > If we are only looking for the shape of the curve, then maybe each > datapoint does not need to be as precise as +/- 10 ELO.
Sorry, but your comment missed the point. My strange result ( MC(4) performs clearly better against MC(1) than expected after knowing the quota for MC(4)-vs-(MC(2) and MC(2)-vs-MC(1) ) is NOT due to fluctuations of small sample size. IT IS A PHENOMENON. Ingo. > > I am not a go programmer, but in experiments with very > > simple model games I found the following phenomenon, > > when looking at the "basic" Monte Carlo algorithm in > > 3 levels: MC(1), MC(2), MC(4). > > [Here MC(k) is the version which makes k random games for each candidate > > move.] > > > > I found several cases (more than 30 % of the games analysed this way) > > with the following phenomenon: > > Elo difference between MC(1) and MC(2) = k(1,2) > > Elo difference between MC(2) and MC(4) = k(2,4). > > Elo difference between MC(1) and MC(4) > k(1,2) + k(2,4) > > > > The games under investigation were so simple that I was able to run > > 100,000 matches in selfplay (or even more), and the inequality above > > ( k(1,4) > k(1,2) + k(2,4) ) was statistically significant. > > > > Ingo -- NEU: FreePhone - kostenlos mobil telefonieren! Jetzt informieren: http://www.gmx.net/de/go/freephone _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
