Personalized Affective Perception via Continual Learning in Human-Agent Negotiation
Overview
This research introduces CLIFER, a continual learning framework for facial expression recognition that adapts valence-arousal perception in real time during human-agent negotiation. In a within-subject study with 66 participants negotiating with a humanoid robot, the CL-enabled agent achieved significantly higher individual utility and elicited higher concession rates compared to the FaceChannel baseline, while maintaining stable Nash distance.
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- arXiv Robotics — research abstractsSignalPersonalized Affective Perception via Continual Learning in Human-Agent Negotiation
This research introduces CLIFER, a continual learning framework for facial expression recognition that adapts valence-arousal perception in real time during human-agent negotiation. In a within-subject study with 66 participants negotiating with a humanoid robot, the CL-enabled agent achieved significantly higher individual utility and elicited higher concession rates compared to the FaceChannel baseline, while maintaining stable Nash distance.
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