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.
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Guid, M., Možina, M., Sadikov, A., Bratko, I. (2010). Deriving Concepts and Strategies from Chess Tablebases. In: van den Herik, H.J., Spronck, P. (eds) Advances in Computer Games. ACG 2009. Lecture Notes in Computer Science, vol 6048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12993-3_18
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DOI: https://doi.org/10.1007/978-3-642-12993-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12992-6
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