An Investigative Study about Application of Supervised Learning for Making Predictions in Chess

Abstract: Supervised learning is not as popular as reinforcement learning in chess programming due to its inability to achieve as high prediction accuracies as reinforcement learning. However, through extensive search by the authors, there seems to be a few numbers of research conducted that focus on applying supervised learning into chess. Therefore, this study investigates how supervised learning could be used to make predictions in chess so that an empirical understanding of supervised learning using both logistic regression and convolutional neural networks is provided. Both the machine learning algorithms will be tested and compared to the prediction accuracies acquired by reinforcement learning through other studies (it will not be implemented in this study). The prediction task was to predict the position from which the next chess piece moves in a chess game. It has been concluded from this study that convolutional neural networks are better at predicting than logistic regression, but had higher tendencies to suffer from overfitting compared to logistic regression. When comparing these two supervised learning algorithms to reinforcement learning, supervised learning algorithms do not achieve as high prediction accuracies as reinforcement learning in general, but could be used as heuristics in various programming contexts in the future. Future research should investigate regularization techniques to overcome with overfitting tendencies in both machine learning algorithms and investigate how data representations may affect the prediction accuracy of respective machine learning algorithm.