Emotion Selection in a Multi-Personality Conversational Agent

Conversational agents and personal assistants represent an historical and important application field in artificial intelligence. This paper presents a novel approach to the problem of humanizing artificial characters by designing believable and unforgettable characters who exhibit various salient emotions in conversations. The proposed model is based on a multi-personality architecture where each agent implements a facet of its identity, each one with its own pattern of perceiving and interacting with the user. In this paper we focus on the emotion selection principle that chooses, from all the candidate responses, the one with the most appropriate emotional state. The experiment shows that a conversational multipersonality character with emotion selection performs better in terms of user engagement than a neutral mono-personality one.