Effectiveness of Conversational Robots Capable of Estimating and Modeling User Values

Abstract

Abstract Personalizing a dialogue system according to the user has been recognized to have various positive effects. Despite the significance of user values, concepts guiding choices and evaluations being recognized in communication, they have not been considered in personalized dialogue systems. Therefore, this study constructs a dialogue system that understands user values through conversation. Furthermore, the impact of understanding values on the interactions between dialogue systems and users is examined. The method is organized with a user model of preferences and values based on the established means-end chain model. We used a large language model (LLM) to estimate values based on the users’ preferences and the reasons they prefer them. Furthermore, an infinite relational model (IRM) estimates the relationships between multiple elements within the user model. The experiments show that the proposed method could estimate user values and enhance animacy and perceived intelligence in users’ impressions of an android robot, prompting new insights into users’ own values. The perception of the robot contributes to improved-quality interactions, and new insights into values facilitate a deeper self-understanding of users. This achievement, demonstrating the effects of using values for interaction, can provide valuable insights into human-robot interaction.