A Conversational Agent that Learns to be Aligned with the Moral Value of Respect"

Abstract

Videogame developers typically conduct user experience surveys to gather feedback from users once they have played. Nevertheless, as users may not recall all the details once finished, we propose an ethical conversational agent that respectfully conducts the survey during gameplay. To achieve this without hindering user’s engagement, we resort to reinforcement learning and an ethical embedding algorithm. Specifically, we transform the learning environment so that it guarantees that the agent learns to be respectful (i.e. aligned with the moral value of respect) while pursuing its individual objective of eliciting as much feedback information as possible. When applying this approach to a simple videogame, our comparative tests between the two agents (ethical and unethical) empirically demonstrate that endowing a survey-oriented conversational agent with this moral value of respect avoids disturbing user’s engagement while still pursuing its individual objective, which is to gather as much information as possible.