Survey of the Use of Multimodal Systems for Chess Game Analysis

Abstract

Multimodal systems in general represent a convergence of technologies and methodologies to compute complex solutions and problems, incorporating computer vision, artificial intelligence, robotics, and neuroscience to enhance the understanding and interaction with the use of machinery in our case. This survey explores recent advancements in the field of chess multimodal systems, focusing on the integration of various modalities—such as optical and neural analysis as well as human-robot interaction to improve on chess understanding, game analysis, and player development. Key topics and additional research include the use of computer vision for real-time scrutiny of physical chess games, the use of association with brain regions in predicting chess expertise and novices’, and the development of chess-playing robots capable of interacting naturally with human opponents. Additionally, the survey highlights the disadvantages, benefits, and future research of applying these multimodal approaches in both industry and educational contexts. By integrating research from diverse avenues, this paper aims to provide an overarching overview of how multimodal systems are shaping the future of chess competitively and casually, offering new opportunities for cognitive research, automated gameplay, and human-robot collaboration.