Interpretation Using Classified Gradient-Based Saliency Maps for Two-Player Board Games

In this study, we propose a gradient-based saliency map generation method to improve the explainability of saliency maps in two-player board games, such as chess and shogi (Japanese chess). In the proposed method, our saliency maps are generated by masking gradients of unnecessary squares to reduce ambiguity and classify the gradient values to express the positive and negative aspects of each piece on a board. The saliency maps can clearly illustrate the roles of each piece in the game. We experimentally evaluate the saliency maps by comparing them against those obtained through conventional methods using chess and shogi board configurations, demonstrating their capacity to effectively identify vital pieces and squares. Thus, we can understand which pieces are important from the visualized saliency maps. This also provides useful insights for human players to learn the games.