Making Superhuman AI More Human in Chess

Computer chess research has traditionally focused on creating the strongest possible chess engine. Recently, however, attempts have been made to create engines that mimic the playing strength and style of human players. Our research proposes enhancements of models developed in this vein that more accurately imitate master-level players, as well as improve the prediction accuracy of existing models on weaker players. Our proposed enhancements are simple to apply by post-processing the output of existing chess engines. The performance of our enhancements was evaluated and compared using two metrics, prediction accuracy and average centipawn loss. We found that using an ensemble model over search depths maximised prediction accuracy, while an evaluation window filtering approach was preferable with respect to average centipawn loss.