Personalized Affective Perception via Continual Learning in Human-Agent Negotiation

Computer Science > Artificial Intelligence

[Submitted on 10 Nov 2026]

Title:Personalized Affective Perception via Continual Learning in Human-Agent Negotiation

View PDF HTML (experimental)Abstract:Self-interested intelligent agents that negotiate with humans should interpret nonverbal expressions and adapt their behavior accordingly. However, generic facial-affect models degrade across individuals and contexts, creating a generalization gap. We introduce an affect-adaptive negotiation strategy that incorporates Continual Learning with Imagination for Facial Expression 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 agent 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 differential feedback for real-time negotiation.

Submission history

From: Mehmet Onur Keskin [view email][v1] Tue, 10 Nov 2026 15:57:45 UTC (30,184 KB)

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