Deriving Concepts and Strategies from Chess Tablebases

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 spe...

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Bibliographic Details
Published inAdvances in Computer Games pp. 195 - 207
Main Authors Guid, Matej, Možina, Martin, Sadikov, Aleksander, Bratko, Ivan
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Optimal Play
Progressive Goal
Rule Trigger
Textbook Instruction
Wrong Corner
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Summary: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.
ISBN:9783642129926
3642129927
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-12993-3_18