Changes

In Arkadiusz Paterek's paper ''Modeling of an evaluation function in games'' <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>, referring his thesis ''Modeling of an evaluation function in chess'', the [[Evaluation|evaluation]] is mentioned using a [[Neural Networks#Perceptron|single-layer perceptron]] design inspired by [[Michael Buro|Michael Buro's]] [[Michael Buro#GLEM|general linear evaluation model]] (GLEM) <ref>[[Michael Buro]] ('''1998'''). ''[http://link.springer.com/chapter/10.1007/3-540-48957-6_8 From Simple Features to Sophisticated Evaluation Functions]''. [[CG 1998]], [https://skatgame.net/mburo/ps/glem.pdf pdf]</ref> in the domain of [[Othello]]. Gusu Gosu performs [[Automated Tuning#LogisticRegression|logistic regression]] to optimize weights of corresponding features aka minimize the [https://en.wikipedia.org/wiki/Mean_squared_error mean squared error] [https://en.wikipedia.org/wiki/Loss_function loss function] by [https://en.wikipedia.org/wiki/Gradient_descent gradient descent] over a set of 6.2 million quiet positions from master games. For each position, it squares the difference of an [[Oracle|oracle]] score of 0.999 for a win, 0.5 for a draw, 0.0013 for a loss, and the [https://en.wikipedia.org/wiki/Dot_product dot product] of the weight and [https://en.wikipedia.org/wiki/Feature_(machine_learning) feature vector], squashed by a [https://en.wikipedia.org/wiki/Logistic_function logistic function] into a 0.0 to 1.0 range of a [[Pawn Advantage, Win Percentage, and Elo|winning probability]]. To speed up matters after [[Automated Tuning|tuning]], an [[Evaluation Hash Table|evaluation cache]] is used along with [[Lazy Evaluation|lazy evaluation]], which performed well in Gosu's [[MTD(f)]] framework.