[3Q5-OS-19b] Affective Computing

[3Q5-OS-19b-04] Preference learning from emotional expressions in pre-play communication contributes a integrative solution between human-AI negotiation

〇Motoaki Sato¹, Kazunori Terada¹, Jonathan Gratch ² (1. Gifu University, 2. University of Southern California)

Keywords:Agent, Emotion, Multi-issue ultimatum game

Embodied AI agents that negotiate with people are needed for proxy negotiation and negotiation training. Various types of information including nonverbal information are exchanged before negotiation so that parties can learn counterpart's preferences and limits, i.e., pre-play communication. However, the relationship between the outcome of negotiation and "emotional pre-play communication", in which parties infer counterpart's preferences and limits from emotional expressions which are considered to be more reliable than verbal information. In the present study, we investigated whether inferring preferences from an AI agent's facial expressions during pre-play communication contributes to an integrative solution in a multi-issue ultimatum game. Participants (n=147) played a multi-issue ultimatum game with an AI agent that expresses its preferences by facial expressions in a (preference learning: present vs. absent) between-participants experiment on-line. The results showed that participants in the preference learning present condition could reach more integrative solutions compared to participants in the preference learning absent condition. This suggests that, in negotiations between humans and AI agents, it is effective to know and model the other party by exchanging nonverbal information before negotiation to reach an integrative solution.