Application of the machine coaching paradigm on chess coaching

Abstract

In the past two decades computer chess has overcome human capabilities and efficiency in all aspects of the game. This impressive achievement has been possible, especially during the last decade, due to state-of-the-art Deep Learning methodologies that have been developed. However, since such methods perform like black-boxes, prohibiting any notion of interpretability by human users in the first place, it would be meaningful to explore the possibility of designing an explainable and cognitively efficient chess bot. In this thesis we present an efficient explainable interaction protocol accompanied by a corresponding user interface for computer chess. Moreover, we also present useful feedback from chess experts – professional players as well as chess coaches.