Towards Deception Detection in a Language-Driven Game

There are many real-world scenarios where agents must reliably detect deceit to make decisions. When deceitful statements are made, other statements or actions may make it possible to uncover the deceit. We describe a goal reasoning agent architecture that supports deceit detection by hypothesizing about an agent’s actions, uses new observations to revise past beliefs, and recognizes the plans and goals of other agents. In this paper, we focus on one module of our architecture, the Explanation Generator, and describe how it can generate hypotheses for a most probable truth scenario despite the presence of false information. We demonstrate its use in a multiplayer tabletop social deception game, One Night Ultimate Werewolf.