Toward Agents That Can Learn Nonverbal Interactive Behavior

Humans are social agents and the social dimension is an important aspect of human cognition. One challenge facing the realization of artifacts and artificial agents that posses humanlike cognition abilities is to implement human-like interactive capabilities into them. Natural Language Processing is one of the earliest applications of AI techniques because of the importance of language in shaping human cognitive and interactive capabilities. Nevertheless nonverbal communication is starting to gain more importance specially in the domains of HRI and ECA because natural human-human communications are known to utilize a variety of nonverbal interaction protocols. This paper proposes a new adaptation algorithm for interactive agents that aims to develop agents that can learn and adapt their theory of mind concerning nonverbal interaction in real-time during actual interactions. The proposed method utilizes elements of the theory of theory and the theory of simulation to guide the adaptation process. A proof of concept simulation experiment with the proposed system is also illustrated.