Knowledge Modelling, Strategy Designing, and Agent Engineering for Reconnaissance Blind Chess

Reconnaissance Blind Chess (RBC) is a unique chess variant where players have limited visibility of a 3x3 square in each round. This paper offers a comparative analysis of the performance of extant agents, along with an assessment of their ability to model their opponents’ knowledge. On the basis of our analytical findings, we propose novel and efficient sensing and movement strategies. Subsequently, these strategies are tested through agent-based gameplay. Furthermore, our experimentation extends to the inference of new knowledge through a strategy based on the Theory of Mind. Collectively, these insights contribute to the selection of the most promising strategies for the design of our Scorca agent. By the time of the paper’s submission, it occupies the second position on the global leaderboard for the RBC game. To conclude, we engage in a discussion of the inherent limitations of the extant agents and offer a glimpse into potential future strategies.