International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 10, No. 4, 8 Jul. 2018
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Statistical Analysis on Result Prediction in Chess
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Author(s)
Paras Lehana
1,*
Sudhanshu Kulshrestha
1
Nitin Thakur
1
Pradeep Asthana
1
Index Terms
Chess, SCID, Chess Engine, PGN, Machine Learning, Linear Regression, Naïve Bayes
Abstract
In this paper, authors have proposed a technique which uses the existing database of chess games and machine learning algorithms to predict the game results. Authors have also developed various relationships among different combinations of attributes like half-moves, move sequence, chess engine evaluated score, opening sequence and the game result. The database of 10,000 actual chess games, imported and processed using Shane’s Chess Information Database (SCID), is annotated with evaluation score for each half-move using Stockfish chess engine running constantly on depth 17. This provided us with a total of 8,40,289 board evaluations. The idea is to make the Multi-Variate Linear Regression algorithm learn from these evaluation scores for same sequence of opening moves and game outcome, then using it to calculate the winning score of a side for each possible move and thus suggesting the move with highest score. The output is also tested with including move details. Game attributes are also classified into classes. Using Naïve Bayes classification, the data result is classified into three classes namely move preferable to white, black or a tie and then the data is validated on 20% of the dataset to determine accuracies for different combinations of considered attributes.
Cite This Paper
Paras Lehana, Sudhanshu Kulshrestha, Nitin Thakur, Pradeep Asthana, "Statistical Analysis on Result Prediction in Chess", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.4, pp. 25-32, 2018. DOI:10.5815/ijieeb.2018.04.04
Reference
[1]About page of Chess-DB.com, biggest online chess database https://chess-db.com/public/about.html accessed on April 2017
[2]The Chess Master and the Computer by Garry Kasparov, The New York Review of Books http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/ accessed on April 2017
[3]Mathematics and chess, Chess.com https://www.chess.com/chessopedia/view/mathematics-and-chess accessed on April 2017
[4]Thrun, Sebastian. "Learning to play the game of chess." Advances in neural information processing systems 7 (1995).
[5]Sutton, Richard S. "Learning to predict by the methods of temporal differences." Machine learning 3.1 (1988): 9-44.
[6]Tadepalli, Prasad. "Planning in games using approximately learned macros." Proceedings of the sixth international workshop on Machine learning. Morgan Kaufmann Publishers Inc., 1989.
[7]GNU Chess, Sponsored by Free Software Foundation https://www.gnu.org/software/chess/ accessed on April 2017
[8]Mannen, Henk. "Learning to play chess using reinforcement learning with database games." CKI Scriptieserie (2003).
[9]Oshri, Barak, and Nishith Khandwala. "Predicting moves in chess using convolutional neural networks."
[10]Bagadia, Sameep, Pranav Jindal, and Rohit Mundra. "Analyzing Positional Play in Chess using Machine Learning." (2014).
[11]Algebraic notation in chess, Wikipedia https://en.wikipedia.org/wiki/Algebraic_notation_(chess) accessed on April 2017
[12]Laws of Chess for competitions starting from 1 July 2014 till 1 July 2017, World Chess Federation https://www.fide.com/fide/handbook.html?id=171&view=article accessed on April 2017
[13]Standard: Portable Game Notation Specification and Implentation Guide, Andreas Saremba and Marie-Theres Saremba http://www.saremba.de/chessgml/standards/pgn/pgn-complete.htm accessed on April 2017
[14][PDF] Analysis Symbol in Chess, Shatranj US http://www.shatranj.us/files/AnalysisSymbols.pdf accessed on April 2017
[15]Elo, Arpad (1978), The Rating of Chessplayers, Past and Present, Arco, ISBN 0-668-04721-6
[16]SCID project on SourceForge http://scid.sourceforge.net/ accessed on April 2017
[17]SCID vs. PC project on SourceForge http://scidvspc.sourceforge.net/ accessed on April 2017
[18]ASUS ROG G751JY product page https://www.asus.com/ROG-Republic-Of-Gamers/ROG-G751JY/ accessed on April 2017
[19]Python library for xlsxwriter, Read the Docs http://xlsxwriter.readthedocs.io/ accessed on April 2017
[20]Encyclopedia of Chess Openings, Chess Informant http://www.chessinformant.org/eco-encyclopedia-of-chess-openings accessed on April 2017
[21]Programmer’s reference, SCID vs. PC http://scidvspc.sourceforge.net/doc/progref.html accessed on April 2017
[22]Givan, Bob, and Ron Parr. "An introduction to Markov decision processes." Purdue University (2001).
[23]First move advantage in chess, Wikipedia https://en.wikipedia.org/wiki/First-move_advantage_in_chess accessed on April 2017
[24]Chess statistics http://www.chessgames.com/chessstats.html accessed on April 2017accessed on April 2017