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.

On Fri, Jun 17, 2011 at 9:31 AM, "Ingo Althöfer" <[email protected]>wrote:

>
> -------- 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).
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