Chess game playing is an important field of machine learning research. With the emergence of powerful chess
engines like Stockfish, Alpha Zero, Leela chess zero, and others. Building a powerful chess engine capable of
playing at a superhuman level is no longer the most challenging task. The majority of these engines still rely on
highly optimised look-ahead algorithms. CNN (convolutional neural networks), which is usually used for photos
and matrix-like data, has proven to be successful at games like chess and go. Chess is being treated as a
regression problem in this project. We suggested a supervised learning strategy employing a convolutional
neural network with a limited look ahead in this research.
Software And Hardware