Evaluating Deep Learning-based Chess-Engine Endgame Strategies - Brito Neto, Epitácio Pessoa de; Menezes e Silva Filho, Telmo de; Rêgo, Thaís Gaudencio do; Malheiros, Yuri (Anais Estendidos do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames); 2023: Anais Estendidos do XXII Simpósio Brasileiro de Jogos e Entretenimento DigitalDO - 10.5753/sbgames_estendido.2023.233983, 2023)
Artificial Intelligence has been used to challenge human players in chess for decades. In 1997, IBM’s Deep Blue won against the best chess player of that time, and since then, the chess engines have continued to improve. However, it is not clear if these new high-performing chess engines are learning how to replicate the way human grandmasters play or if they are devising new strategies to win. Therefore, in this paper, we evaluated two chess engines that use deep learning approaches: StockFish NNUE and Lc0, to compare their moves in endgame situations to the moves in chess theory books. We collected 19 types of endgames and we ran the engines to replicate the books’ moves. After that, we computed the similarity, that is, the percentage of equal moves. The Lc0 engine has 40.20% similarity in our experiments and StockFish NNUE 22.50%. These results show that the engines replicate some moves from chess-theory books, but they differ in most parts from what is expected from human players.