A Hybrid Gomoku Deep Learning Artificial Intelligence

Abstract

Gomoku is an ancient board game. The traditional approach to solving the Gomoku is to apply tree search on a Gomoku game tree. Although the rules of Gomoku are straightforward, the game tree complexity is enormous. Unlike other board games such as chess and Shogun, the Gomoku board state is more intuitive. This feature is similar to another famous board game, the game of Go. The success of AlphaGo [5, 6] inspired us to apply a supervised learning method and deep neural network in solving the Gomoku game. We designed a deep convolutional neural network model to help the machine learn from the training data. In our experiment, we got 69% accuracy on the training data and 38% accuracy on the testing data. Finally, we combined the trained deep neural network model with a hard-coded convolution-based Gomoku evaluation function to form a hybrid Gomoku artificial intelligence (AI) which further improved performance.