Adapting conversational strategies to co-optimize agent's task performance and user's engagement

In this work, we present a socially interactive agent able to adapt its conversational strategies to maximize user's engagement during the interaction. For this purpose, we train our agent with simulated users using deep reinforcement learning. First, the agent estimates the simulated user's engagement depending on the latter's nonverbal behaviors and turn-taking status. This measured engagement is then used as a reward to balance the task of the agent (giving information) and its social goal (maintaining the user highly engaged). Agent's dialog acts may have different impact on the user's engagement depending on the latter's conversational preferences.