Deriving concepts and strategies from chess tablebases

Authors:
Matej Guid;Martin Možina;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
Venue:
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Year:
2009

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Abstract

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.