[Computer-go] CLOP: Confident Local Optimization for Noisy

Thomas Marius Schneider marius.rks at googlemail.com
Thu Sep 1 05:12:24 PDT 2011


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Hi,

the abstract sounds interesting. I think I will read it this evening.

But do you know the publications about parameter tuning for boolean
constraint solver (e.g. SAT Solver) from Holger Hoos et al?
e.g. the phd thesis of Frank Hutter
http://www.cs.ubc.ca/~hutter/papers/Hutter09PhD.pdf

I think the problems of player tuning and solver tuning are quite
similar, or?

Cheers,
Marius

> Hi,
> 
> This is a draft of the paper I will submit to ACG13.
> 
> Title: CLOP: Confident Local Optimization for Noisy Black-Box Parameter Tuning
> 
> Abstract: Artificial intelligence in games often leads to the problem of parameter tuning. Some heuristics may have coefficients, and they should be tuned to maximize the win rate of the program. A possible approach consists in building local quadratic models of the win rate as a function of program parameters. Many local regression algorithms have already been proposed for this task, but they are usually not robust enough to deal automatically and efficiently with very noisy outputs and non-negative Hessians. The CLOP principle, which stands
> for Confident Local OPtimization, is a new approach to local regression that overcomes all these problems in a simple and efficient way. It consists in discarding samples whose estimated value is confidently inferior to the mean of all samples. Experiments demonstrate that, when the function to be optimized is smooth, this method outperforms all other tested algorithms.
> 
> pdf and source code:
> http://remi.coulom.free.fr/CLOP/
> 
> Comments, questions, and suggestions for improvement are welcome.
> 
> R?mi


- -- 
Thomas Marius Schneider
Institute of Computer Science
University of Potsdam, Germany
Tel +493319773087
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