Deriving Concepts and Strategies from Chess Tablebases

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 argumentbased 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.

• Type: Conference
• Year: 2009
• Where: ACG
• Authors: Matej Guid, Martin Mozina, Aleksander Sadikov, Ivan Bratko
• Keywords: ACG 2009, Chess Endgames, Complete Tablebases, Computer Graphics, Lone King Endgame