Learned Appraisal Models for Affective Reasoning

Develop learned appraisal functions for mapping agent events to affective states in VIGIL, trained from user feedback, interaction traces, or downstream utility signals, to achieve more adaptive and personalized affective reasoning beyond rule-based heuristics.

Background

EmoBank’s current affective appraisals rely on deterministic, rule-based mappings from event semantics to emotions, valence, and intensity. This approach ensures interpretability but may limit adaptability across contexts and users.

The authors propose exploring learned appraisal models, potentially leveraging feedback and interaction data to personalize affective signals, which could enhance diagnostic sensitivity and remediation relevance.