-------- Original-Nachricht -------- > Datum: Fri, 17 Jun 2011 11:58:45 +0100
Von: "Jacques Basaldúa" <[email protected]> > ... > 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. 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 PS. In this context I would also like to mention (once more) my technical report on the basin structures in self-play. See at http://www.althofer.de/monte-carlo-basins-althoefer.pdf So, the winning quota at double resources does not behave monotonically (for very quick searches). -- 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
