Generative Model of Agent’s Behaviors in Human-Agent Interaction
Soumia Dermouche, Catherine Pélachaud · 2019
A social interaction implies a social exchange between two or more persons, where they adapt and adjust their behaviors in response to their interaction partners. With the growing interest in human-agent interactions, it is desirable to make these interactions more natural and human like. In this context, we aim at enhancing the quality of the interaction between user and Embodied Conversational Agent (ECA) by endowing ECA with the capacity to adapt its behavior in real time according the user’s behavior. The novelty of our approach is to model the agent’s nonverbal behaviors as a function of both agent’s and user’s behaviors jointly with the agent’s communicative intentions creating a dynamic loop between both interactants. Moreover, we encompass the variation of behavior over time through a LSTM-based model. Our model IL-LSTM (Interaction Loop LSTM) predicts the next agent’s behavior taking into account the behavior that both, the agent and the user, have displayed within a time window. We have conducted an evaluation study involving an agent interacting with visitors in a science museum. Results of our study show that participants have better experience and are more engaged in the interaction when the agent adapts its behaviors to theirs, thus creating an interactive loop.