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Automated Tuning

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| style="vertical-align:top;" | Since the relationship between [[Pawn Advantage, Win Percentage, and Elo|win percentage and pawn advantage]] is assumed to follow a [ logistic model], one may treat static evaluation as [[Neural Networks#Perceptron|single-layer perceptron]] or single [ neuron] [[Neural Networks|ANN]] with the common [ logistic] [ activation function], performing the perceptron algorithm to train it <ref>[ Re: Piece weights with regression analysis (in Russian)] by [[Fabien Letouzey]], [[CCC]], May 04, 2015</ref>. [ Logistic regression] in evaluation tuning was first elaborated by [[Michael Buro]] in 1995 <ref>[[Michael Buro]] ('''1995'''). ''[ Statistical Feature Combination for the Evaluation of Game Positions]''. [ JAIR], Vol. 3</ref>, and proved successful in the game of [[Othello]] in comparison with [[Mathematician#RFisher|Fisher's]] [ linear discriminant] and quadratic [ discriminant] function for [ normally distributed] features, and served as eponym of his Othello program ''Logistello'' <ref>[ LOGISTELLO's Homepage]</ref>. In computer chess, logistic regression was applied by [[Arkadiusz Paterek]] with [[Gosu]] <ref>[[Arkadiusz Paterek]] ('''2004'''). ''Modelowanie funkcji oceniającej w grach''. [[University of Warsaw]], [ zipped ps] (Polish, Modeling of an evaluation function in games)</ref>, later proposed by [[Miguel A. Ballicora]] in a 2009 [[CCC]] post, as applied to used by [[Gaviota]] <ref>[ Re: Insanity... or Tal style?] by [[Miguel A. Ballicora]], [[CCC]], April 02, 2009</ref>, was independently described by [[Amir Ban]] in 2012 for [[Junior|Junior's]] evaluation learning <ref>[[Amir Ban]] ('''2012'''). ''[ Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [ The Hebrew University of Jerusalem] - Center for the Study of Rationality, [ Givat Ram]</ref>, and explicitly mentioned by [[Álvaro Begué]] in a January 2014 [[CCC]] discussion <ref>[ Re: How Do You Automatically Tune Your Evaluation Tables] by [[Álvaro Begué]], [[CCC]], January 08, 2014</ref>, when [[Peter Österlund]] explained [[Texel's Tuning Method]] <ref>[ The texel evaluation function optimization algorithm] by [[Peter Österlund]], [[CCC]], January 31, 2014</ref>, which subsequently popularized logistic regression tuning in computer chess. [[Vladimir Medvedev|Vladimir Medvedev's]] [[Point Value by Regression Analysis]] <ref>[ Определяем веса шахматных фигур регрессионным анализом / Хабрахабр] by [[Vladimir Medvedev|WinPooh]], April 27, 2015 (Russian)</ref> <ref>[ Piece weights with regression analysis (in Russian)] by [[Vladimir Medvedev]], [[CCC]], April 30, 2015</ref> experiments showed why the [ logistic function] is appropriate, and further used [ cross-entropy] and [ regularization].
| [[FILE:SigmoidTexelTune.gif|border|left|thumb|baseline|300px|link=|[ Logistic function] <ref>[ log-linear 1 / (1 + 10^(-s/4)) , s=-10 to 10] from [ Wolfram|Alpha]</ref> ]]
* [[Arasan#Tuning|Arasan's Tuning]]
* [[Eval Tuning in Deep Thought]]
* [[Gosu]]
* [[Minimax Tree Optimization]] (MMTO or the Bonanza-Method in [[Shogi]])
* [[Point Value by Regression Analysis]]
* [[Mathieu Autonès]], [[Aryel Beck]], [[Phillippe Camacho]], [[Nicolas Lassabe]], [[Hervé Luga]], [[François Scharffe]] ('''2004'''). ''[ Evaluation of Chess Position by Modular Neural network Generated by Genetic Algorithm]''. [ EuroGP 2004]
* [[Henk Mannen]], [[Marco Wiering]] ('''2004'''). ''Learning to play chess using TD(λ)-learning with database games''. [ Cognitive Artificial Intelligence], [ Utrecht University], Benelearn’04
* [[Arkadiusz Paterek]] ('''2004'''). ''Modelowanie funkcji oceniającej w szachach''. Masters thesis, [[University of Warsaw]] (Polish, Modeling of an evaluation function in chess)
* [[Arkadiusz Paterek]] ('''2004'''). ''Modelowanie funkcji oceniającej w grach''. [[University of Warsaw]], [ zipped ps] (Polish, Modeling of an evaluation function in games)
==2005 ...==
* [[Dave Gomboc]], [[Michael Buro]], [[Tony Marsland]] ('''2005'''). ''Tuning Evaluation Functions by Maximizing Concordance''. [ Theoretical Computer Science], Vol. 349, No. 2, [ pdf]

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