Arthur: a new ECA that uses Memory to improve Communication
This addresses communication enhancement in human-agent interaction, but appears incremental as it combines existing components like memory and emotion detection.
The researchers developed an embodied conversational agent named Arthur that uses facial recognition, emotion detection, and an artificial memory system based on a human model to improve communication with users. Experiments showed the model had a consistent impact on users, though no specific quantitative results were provided.
This article proposes an embodied conversational agent named Arthur. In addition to being able to talk to a person (using text and voice), he is also able to recognize the person he is talking to and detect his/her expressed emotion through facial expressions. Arthur uses these skills to improve communication with the user, also using his artificial memory, which stores and retrieves data about events and facts, based on a human memory model. We conducted some experiments to collect quantitative and qualitative information, which show that our model provides a consistent impact on users.