On conversational agents with mental states

Abstract

Embodied conversational agents (ECAs) have been put forward as a promising means for the training of social skills. The traditional approach to drive the behaviour of ECAs during human-agent dialogues is to use conversation trees. Although this approach is easy to use and very transparent, an important limitation of conversation trees is that the resulting behaviour of the ECAs is often perceived as predictable. To provide ECAs with more sophisticated behaviour, the current paper proposes an approach to endow them with mental states. The approach is illustrated by a motivational example in the domain of aggression de-escalation training.