Changing Topics of Dialogue for Natural Mixed-initiative Interaction of Conversational Agent based on Human Cognition and Memory

Abstract

Mixed-initiative interaction (MII) plays an important role in conversation agent. In the former MII research, MII process only static conversation and cannot change the conversation topic dynamically by the system because the agent depends only on the working memory and predefined methodology. In this paper, we propose the mixed-initiative interaction based on human cognitive architecture and memory structure. Based on the global workspace theory, one of the cognitive architecture models, proposed method can change the topic of conversation dynamically according to the long term memory which contains past conversation. We represent the long term memory using semantic network which is a popular representation for storing knowledge in the field of cognitive science, and retrieve the semantic network according to the spreading activation theory which has been proven to be efficient for inferring in semantic networks. Through some dialogue examples, we show the usability of the proposed method.