Deriving Concepts and Strategies from Chess Tablebases

  • Matej Guid
  • Martin Možina
  • Aleksander Sadikov
  • Ivan Bratko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6048)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Matej Guid
    • 1
  • Martin Možina
    • 1
  • Aleksander Sadikov
    • 1
  • Ivan Bratko
    • 1
  1. 1.Artificial Intelligence Laboratory, Faculty of Computer and Information ScienceUniversity of LjubljanaSlovenia

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