Modeling agents with a theory of mind: Theory-theory versus simulation theory

Abstract

Virtual training systems with intelligent agents provide an effective means to train people for complex, dynamic tasks like crisis management or firefighting. For successful training, intelligent virtual agents should be able to show believable behavior, adapt their behavior to the trainee's performance and give useful explanations about their behavior. Agents can provide more believable behavior and explanations if they, besides their own, take the assumed knowledge and intentions of other players in the scenario into account. This paper proposes two ways to model agents with a theory of mind, i.e. equip them with the ability to ascribe mental concepts such as knowledge and intentions to others. The first theory of mind model is based on theory--theory TT and the second on simulation theory ST. In a simulation study, agents with no theory of mind, a TT-based theory of mind, and an ST-based theory of mind are compared. The results show that agents with a theory of mind are preferred over agents with no theory of mind, and that, regarding agent development, the ST model has advantages over the TT model.