Changes

The supervised problem of regression applied to [[Automated Tuning#MoveAdaption|move adaptation]] was used by [[Thomas Nitsche]] in 1982, minimizing the [https://en.wikipedia.org/wiki/Mean_squared_error mean squared error] of a cost function considering the program’s and a grandmaster’s choice of moves, as mentioned, extended by [[Tony Marsland]] in 1985, and later by the [[Deep Thought]] team. Regression used to [[Automated Tuning#ValueAdaption|adapt desired values]] was described by [[Donald H. Mitchell]] in his 1984 masters master thesis on evaluation features in [[Othello]], cited by [[Michael Buro]] <ref>[[Michael Buro]] ('''1995'''). ''[httphttps://www.jair.org/papersindex.php/jair/paper179.html article/view/10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3</ref> <ref>[[Donald H. Mitchell]] ('''1984'''). ''Using Features to Evaluate Positions in Experts' and Novices' Othello Games''. Masters Master thesis, Department of Psychology, [[Northwestern University]], Evanston, IL</ref>. [[Jens Christensen]] applied [https://en.wikipedia.org/wiki/Linear_regression linear regression] to chess in 1986 to learn [[Point Value|point values]] in the domain of [[Temporal Difference Learning|temporal difference learning]] <ref>[[Jens Christensen]] ('''1986'''). ''[http://link.springer.com/chapter/10.1007/978-1-4613-2279-5_9?no-access=true Learning Static Evaluation Functions by Linear Regression]''. in [[Tom Mitchell]], [[Jaime Carbonell]], [[Ryszard Michalski]] ('''1986'''). ''[http://link.springer.com/book/10.1007/978-1-4613-2279-5 Machine Learning: A Guide to Current Research]''. The Kluwer International Series in Engineering and Computer Science, Vol. 12</ref>.

Since the relationship between [[Pawn Advantage, Win Percentage, and Elo|win percentage and pawn advantage]] is assumed to follow a [https://en.wikipedia.org/wiki/Logistic_model logistic model], one may treat static evaluation as [[Neural Networks#Perceptron|single-layer perceptron]] or single [https://en.wikipedia.org/wiki/Artificial_neuron neuron] [[Neural Networks|ANN]] with the common [https://en.wikipedia.org/wiki/Logistic_function logistic] [https://en.wikipedia.org/wiki/Activation_function activation function], performing the perceptron algorithm to train it <ref>[http://www.talkchess.com/forum/viewtopic.php?t=56168&start=36 Re: Piece weights with regression analysis (in Russian)] by [[Fabien Letouzey]], [[CCC]], May 04, 2015</ref>. [https://en.wikipedia.org/wiki/Logistic_regression Logistic regression] in evaluation tuning was first elaborated by [[Michael Buro]] in 1995 <ref>[[Michael Buro]] ('''1995'''). ''[httphttps://www.jair.org/papersindex.php/jair/article/paper179.html view/10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3</ref>, and proved successful in the game of [[Othello]] in comparison with [[Mathematician#RFisher|Fisher's]] [https://en.wikipedia.org/wiki/Kernel_Fisher_discriminant_analysis linear discriminant] and quadratic [https://en.wikipedia.org/wiki/Discriminant discriminant] function for [https://en.wikipedia.org/wiki/Normal_distribution normally distributed] features, and served as eponym of his Othello program ''Logistello'' <ref>[https://skatgame.net/mburo/log.html 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]], [https://www.mimuw.edu.pl/~paterek/mfog.ps.gz zipped ps] (Polish, Modeling of an evaluation function in games)</ref>, later proposed by [[Miguel A. Ballicora]] in 2009 as used by [[Gaviota]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=27266&postdays=0&postorder=asc&topic_view=&start=11 Re: Insanity... or Tal style?] by [[Miguel A. Ballicora]], [[CCC]], April 02, 2009</ref>, independently described by [[Amir Ban]] in 2012 for [[Junior|Junior's]] evaluation learning <ref>[[Amir Ban]] ('''2012'''). ''[http://www.ratio.huji.ac.il/node/2362 Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [https://en.wikipedia.org/wiki/Hebrew_University_of_Jerusalem The Hebrew University of Jerusalem] - Center for the Study of Rationality, [https://en.wikipedia.org/wiki/Givat_Ram Givat Ram]</ref>, and explicitly mentioned by [[Álvaro Begué]] in a January 2014 [[CCC]] discussion <ref>[http://www.talkchess.com/forum/viewtopic.php?t=50823&start=10 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>[http://www.talkchess.com/forum/viewtopic.php?topic_view=threads&p=555522&t=50823 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>[http://habrahabr.ru/post/254753/ Определяем веса шахматных фигур регрессионным анализом / Хабрахабр] by [[Vladimir Medvedev|WinPooh]], April 27, 2015 (Russian)</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=56168 Piece weights with regression analysis (in Russian)] by [[Vladimir Medvedev]], [[CCC]], April 30, 2015</ref> experiments showed why the [https://en.wikipedia.org/wiki/Logistic_function logistic function] is appropriate, and further used [https://en.wikipedia.org/wiki/Cross_entropy cross-entropy] and [https://en.wikipedia.org/wiki/Regularization_%28mathematics%29 regularization].

* [[Donald H. Mitchell]] ('''1984'''). ''Using Features to Evaluate Positions in Experts' and Novices' Othello Games''. Masters Master thesis, Department of Psychology, [[Northwestern University]], Evanston, IL

* [[Michael Buro]] ('''1995'''). ''[httphttps://www.jair.org/papersindex.php/jair/article/paper179.html view/10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3