Hi,

Thanks for the link. I was not aware of the work of Frank Hutter. It is indeed 
similar, and looks interesting. I will take a look.

Rémi

On 1 sept. 2011, at 14:12, Thomas Marius Schneider wrote:

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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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