Personalized Affective Perception via Continual Learning in Human-Agent Negotiation
Personalized 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.
The study demonstrates that coupling continual affective personalization with differential feedback improves individual utility and concession rates in human-robot negotiation, though Nash distance remained unchanged.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ression Recognition (CLIFER) to personalize valence-arousal perception in real time. Differential feedback converts changes in valence-arousal direction and intensity into adjustments of the agent's target utility. An asynchronous architecture updates CLIFER in the background during live interaction. In a counterbalanced within-subject study in which 66 participants negotiated with a humanoid robot, the CL-enabled ag
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ent achieved significantly higher individual utility and elicited higher participant concession rates than the FaceChannel baseline. The CL condition yielded significantly higher mean arousal estimates, alongside a broader output range. Participant utility and Nash distance did not change significantly between conditions. These outcomes support the practical value of coupling continual affective personalization with
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Source：arXiv Robotics — research abstracts · arxiv.org