Analysis of Strategic Evolution Structure of 5-player Werewolf Based on Simulation

Abstract

Games are widely used as a benchmark in the field of artificial intelligence research due to the clarity of the rules and sufficient complexity. In recent years, the game "werewolf" has received a lot of attention in the field of artificial intelligence. Werewolf is an incomplete information game that is played through dialogue between the players. The artificial intelligence that plays werewolves is called "AIWolf". A platform for AIWolf has been created, and AIWolf competitions have been held to test the performance of the agents running on the platform. In the AIWolf competition, general developers are invited to submit AIWolf agents, which are expected to improve the performance of AIWolf through collective intelligent development. In this study, we first analyzed the logs of previous competitions and found that the agents from previous competitions have evolved to be stronger than the agents created before them. Furthermore, based on the strategy evolution derived from the analysis, we propose a simulation method for 5-player werewolf to investigate whether repeated evolution converges to the dominant strategy. The simulation results show that no dominant strategy is found and that the strategies change continuously and periodically. Our results show that werewolf games are sufficiently complex to be the subject of artificial intelligence research, which we believe will help us to understand the structure of werewolf games and discover appropriate strategies.