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.
₹10000 (INR)
NON IEEE -2022