Learning Positional Features for Annotating Chess Games: A Case Study

Authors:
Matej Guid;Martin Možina;Jana Krivec;Aleksander Sadikov;Ivan Bratko
Affiliations:
Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Venue:
CG '08 Proceedings of the 6th international conference on Computers and Games
Year:
2008

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Abstract

By developing an intelligent computer system that will provide commentary of chess moves in a comprehensible, user-friendly, and instructive way, we are trying to use the power demonstrated by the current chess engines for tutoring chess and for annotating chess games. In this paper, we point out certain differences between the computer programs which are specialized for playing chess and our program which is aimed at providing quality commentary. Through a case study, we present an application of argument-based machine learning, which combines the techniques of machine learning and expert knowledge, to the construction of more complex positional features, in order to provide our annotating system with an ability to comment on various positional intricacies of positions in the game of chess.