A Rule-Based AI Method for an Agent Playing Big Two

Abstract

Big Two is a popular multiplayer card game in Asia. This research proposes a new method, named rule-based AI, for an agent playing Big Two. The novel method derives rules based on the number of cards the AI agent has left and the control position. The rules for two to four cards left are used to select the card combination to discard based on the number of cards remaining in the agent’s hand. The rules for more than four cards left conditionally prioritize discarding the card combination in the classified cards with lower priority. A winning strategy provides guidelines to guarantee that the AI agent will win when a win is achievable within three moves. We also design the rules for the AI agent without control for holding cards and splitting cards. The experimental results show that our proposed AI agent can play Big Two well and outperform randomized AI, conventional AI, and human players, presenting winning rates of 89.60%, 73.00%, and 55.05%, respectively, with the capability of maximizing the winning score and minimizing the number of cards left when the chance of winning is low.