Dialog management of healthcare consulting system by utilizing deceptive information

Abstract

In the past few years, there has been an increasing number of works on negotiation dialog. These studies mainly focus on situations where interlocutors work cooperatively to agree on a mutual objective that can fulfill each of their own requirements. However, in real-life negotiation, such situations do not happen all the time, and participants can tell lies to gain an advantage. In this research, we propose a negotiation dialog management system that detects when a user is lying and a dialog behavior for how the system should react when faced with a lie. We design our system for a living habits consultation scenario, where the system tries to persuade users to adopt healthy living habits. We show that we can use the partially observable Markov decision process (POMDP) to model this conversation and use reinforcement learning to train the system's policy. Our experimental results demonstrate that the dialog manager considering deceptive states outperformed a dialog manager without this consideration in terms of the accuracy of action selection, and improved the true success rate of the negotiation in the healthcare consultation domin.