Adaptation is a key mechanism in human–human interaction. In our work, we aim at endowing embodied conversational agents with the ability to adapt their behavior when interacting with a human interlocutor. With the goal to better understand what the main challenges concerning adaptive agents are, we investigated the effects on the user’s experience of three adaptation models for a virtual agent. The adaptation mechanisms performed by the agent take into account the user’s reaction and learn how to adapt on the fly during the interaction. The agent’s adaptation is realized at several levels (i.e., at the behavioral, conversational, and signal levels) and focuses on improving the user’s experience along different dimensions (i.e., the user’s impressions and engagement). In our first two studies, we aim to learn the agent’s multimodal behaviors and conversational strategies to dynamically optimize the user’s engagement and impressions of the agent, by taking them as input during the learning process. In our third study, our model takes both the user’s and the agent’s past behavior as input and predicts the agent’s next behavior. Our adaptation models have been evaluated through experimental studies sharing the same interacting scenario, with the agent playing the role of a virtual museum guide. These studies showed the impact of the adaptation mechanisms on the user’s experience of the interaction and their perception of the agent. Interacting with an adaptive agent vs. a nonadaptive agent tended to be more positively perceived. Finally, the effects of people’s a priori about virtual agents found in our studies highlight the importance of taking into account the user’s expectancies in human–agent interaction.
Domain and Subtask-Adaptive Conversational Agents to Provide an Enhanced Human-Agent Interaction
