A Survey on Chess Engine Using Deep Learning

Abstract

 Chess games have been avantgarde in the history of artificial intelligence. Traditional chess engines such as Stock fish and Komodo have been developed using heuristics with thousands of rules handcrafted by human chess professionals. However, in recent developments, Alpha Zero adopts a completely different strategy and replaces these handmade rules with deep neural networks. In addition, the generic Algorithm used to develop Alpha Zero knows nothing beyond the basic rules of the game, but it achieved superhuman performance in chess, go and shogi games by learning from self-playing games. In addition, you can see how the chess engine evolved over time using different technology and algorithms. In addition, alpha zero is a method of generalizing the algorithm of chess, shogi (shogi), and Go. Starting with random play, I studied how not to be given domain knowledge other than game rules, and Alpha Zero became the best in chess games in a few hours of self-learning. Finally, by observing how the use of deep learning affects the performance of the chess engine and the movement of chess games played on these two engines, Alpha Zero is summarized as compared to other traditional chess engines.