[Computer-go] CLOP: Confident Local Optimization for NoisyBlack-Box Parameter Tuning

Brian Sheppard sheppardco at aol.com
Sat Sep 10 08:20:15 PDT 2011

I am going through the paper, and there is a point where I do not

When the weights are recalculated in Algorithm 1, the expression for wk is
exp((qk(x) - mk) / H * sk).

Should the formula have a square? That is, exp((qk(x) - mk) * (qk(x) - mk) /
H * sk)?


-----Original Message-----
From: computer-go-bounces at dvandva.org
[mailto:computer-go-bounces at dvandva.org] On Behalf Of Rémi Coulom
Sent: Thursday, September 01, 2011 6:01 AM
To: computer-go at dvandva.org
Subject: [Computer-go] CLOP: Confident Local Optimization for NoisyBlack-Box
Parameter Tuning


This is a draft of the paper I will submit to ACG13.

Title: CLOP: Confident Local Optimization for Noisy Black-Box Parameter

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:

Comments, questions, and suggestions for improvement are welcome.

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