Difference between revisions of "Gosu"
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| − | 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]]. | + | 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]]. 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. |
=Tournament Play= | =Tournament Play= | ||
Revision as of 22:25, 21 March 2019
Gosu,
a Chess Engine Communication Protocol compatible chess engine by Arkadiusz Paterek, originated as a part of his masters thesis. In Korean its name means "expert".
Contents
Description
In Arkadiusz Paterek's paper Modeling of an evaluation function in games [2], referring his thesis Modeling of an evaluation function in chess, the evaluation is mentioned using a single-layer perceptron design inspired by Michael Buro's general linear evaluation model (GLEM) [3] in the domain of Othello. Gosu performs logistic regression to optimize weights of corresponding features aka minimize the mean squared error loss function by gradient descent over a set of 6.2 million quiet positions from master games. For each position, it squares the difference of an oracle score of 0.999 for a win, 0.5 for a draw, 0.0013 for a loss, and the dot product of the weight and feature vector, squashed by a logistic function into a 0.0 to 1.0 range of a winning probability. To speed up matters after tuning, an evaluation cache is used along with lazy evaluation, which performed well in Gosu's MTD(f) framework.
Tournament Play
Gosu played four Polish Computer Chess Championships, after a strong debut at the PCCC 2004, it won the PCCC 2005, and became third at the PCCC 2006 and played the IOPCCC 2007 where it lost the final rounds versus later winner Glaurung and runner up WildCat. Gosu further performed at the CCT7 with 4½/8.
Publications
- 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)
Forum Posts
2004
- Gosu by Grzegorz Sidorowicz, Winboard Forum, September 15, 2004
- gosu v.0.2 by Arek Paterek, Winboard Forum, September 20, 2004
- Gosu does not find the mate in 1... by Dr. Axel Schumacher, Winboard Forum, October 03, 2004
2005
- Gauntlet Gosu v0.6 with games by Karl-Heinz Söntges, CCC, February 04, 2005
- Gauntlet Gosu v0.7 with clear improvement - games by Karl-Heinz Söntges, CCC, April 12, 2005
- Gosu 0.8 by Telmo Escobar, Winboard Forum, April 29, 2005
- Gauntlet Gosu v0.8 - games by Karl-Heinz Söntges, CCC, May 10, 2005
- Gauntlet Gosu v0.9 - games - Fafis was removed by Karl-Heinz Söntges, CCC, May 30, 2005
2006 ...
- Problem with new Gosu Postby Tony Thomas, Winboard Forum, August 05, 2006
- Gosu loosing on time by Volker Pittlik, Winboard Forum, August 06, 2006
- Gosu 0.16 v Thor's Hammer 2.28 (queen sac) by Graham Banks]], CCC, December 06, 2008
External Links
Chess Engine
- Index of /chess/engines/Norbert's collection/Gosu (Compilation) by Norbert Raimund Leisner, hosted by Kirill Kryukov
Misc
References
- ↑ Pansori gosu from Wikipedia
- ↑ Arkadiusz Paterek (2004). Modelowanie funkcji oceniającej w grach. University of Warsaw, zipped ps (Polish, Modeling of an evaluation function in games)
- ↑ Michael Buro (1998). From Simple Features to Sophisticated Evaluation Functions. CG 1998, pdf
