Challenging Artificial Intelligence With Multiopponent and Multimovement Prediction for the Card Game Big2

Abstract

Big2 is one of the most popular card games in Chinese residential regions; however, there is lack of advanced computer players with challenging artificial intelligence. In this study, we propose the Big2 artificial intelligence (Big2AI) framework consisting of card superiority analysis, dynamic weight adjustment, game feature learning, and multi-opponent movement prediction based on Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS). According to our review of relevant research, this is the first artificial intelligence framework that can perform self-playing with various computer players, improve win rates through historical game features, and predict multiple movements of multiple opponents in the card game Big2. An Android-based prototype of four-player Big2 game is implemented to verify the feasibility and superiority of Big2AI. Experimental results show that Big2AI outperforms existing artificial intelligence and can achieve the highest win rate and the least losing points against computer and human players in Big2 games.