A Synthetic Player for Ayo Board Game Using Alpha-Beta Search and Learning Vector Quantization

摘要

Game playing especially, Ayo game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage significant interest of users. This study presents a synthetic player (Ayo) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm.