Abstract
Supervised learning requires large collections of accurately labeled examples. While chess engines and endgame tablebases can provide perfect evaluations, building datasets dedicated to learning a specific chess skill remains a separate challenge.
This paper presents a generic framework for automatically generating exhaustive labeled datasets over a finite state space. The proposed approach is independent of existing tablebases and relies on recursive propagation from an initial set of solved positions.