Complete tablebases, indicating best moves for every position, exist for chess endgames. There is no doubt that tablebases contain a wealth of knowledge, however, mining for this knowledge, manually or automatically, proved as extremely difficult. Recently, we developed an approach that combines specialized minimax search with the argument-based machine learning (ABML) paradigm. In this paper, we put this approach to test in an attempt to elicit human-understandable knowledge from tablebases. Specifically, we semi-automatically synthesize knowledge from the KBNK tablebase for teaching the difficult king, bishop, and knight versus the lone king endgame. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Guid, M., Možina, M., Sadikov, A., & Bratko, I. (2010). Deriving concepts and strategies from chess tablebases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6048 LNCS, pp. 195–207). https://doi.org/10.1007/978-3-642-12993-3_18
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