On Sat, Nov 23, 2013 at 11:32:49AM +0100, Detlef Schmicker wrote: > Just to let you know: > > I did a comparison of the playings strength vs. playouts. > > This time I used 4 times the oakfoam playouts for pachi > (eg. 1000 for oakfoam 4000 for pachi) > > The graph shows how bad we become (in comparison) with more playouts:(. > >From the games the first impression is, that the joseki becomes worse > with more playouts e.g. > > http://www.physik.de/playouts2.pdf > The plot is 1050 games fitted with a 5th order polynome. The borders of > the plot are not statistical significant!
It really is mainly a question of tuning of parameters, I'd believe - whether you are tuning with short or long games. C.f. tables 1 and 3 in http://pasky.or.cz/go/pachi-tr.pdf . In particular, tuning with small number of playouts will favor very strong bias - favoring quick and decisive solutions to local situations, that will however prevent long-term convergence in cases where your heuristics are wrong. Sometimes, this phenomenon becomes very arcane. For example, our smart implementation of dynamic komi works great with "low-end" or "mid-end" computing power, but increasing computing power further, its impact decreases almost to negative at extremely high end; we didn't figure out why that happens. One method that remains mostly unexplored in literature AFAIK is dynamically adjusting engine parameters based on elapsed/available time; certainly something good to explore and publish. I'm not sure if anything but such an explicit approach could get best performance both with low and high computing power. Petr "Pasky" Baudis _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
