Learning positional features for annotating chess games: A case study

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. © 2008 Springer-Verlag Berlin Heidelberg.