[Empirical paper track] Deciphering Enemies in the Darkness through Modeling and Examination of Knowledge in Reconnaissance Blind Chess

An important research topic about Theory of Mind (ToM) is the ability to understand and reason about how agents acquire and predict the behavioral and mental states of other agents in dynamic environments, especially those involving a significant change in knowledge and information. In this paper, we focus on the modeling and examination of knowledge of other agents in imperfect information games. More specifically, we delve into the nuances of the change of knowledge in the Reconnaissance Blind Chess (RBC). In each round, players are granted limited sensing capacity of the board. Thus, the understanding opponent's knowledge and strategy plays a key role in decision-making in each round. This paper studies how an agent can model and utilize an opponent's knowledge in the RBC game. The examination includes a detailed comparison of information obtained through different actions in the game. We design two sensing strategies for obtaining information based on entropy and other factors and compare how these strategies can impact the outcome of the game. Finally, we discuss how our research results could be generalized to the understanding of opponents' knowledge and behavior in non-cooperative imperfect information games.