[2604.00904] Fatigue-Aware Learning to Defer via Constrained Optimisation

Computer Science > Machine Learning arXiv:2604.00904 (cs) [Submitted on 1 Apr 2026] Title:Fatigue-Aware Learning to Defer via Constrained Optimisation Authors:Zheng Zhang, Cuong C. Nguyen, David Rosewarne, Kevin Wells, Gustavo Carneiro View a PDF of the paper titled Fatigue-Aware Learning to Defer via Constrained Optimisation, by Zheng Zhang and 4 other authors View PDF Abstract:Learning to defer (L2D) enables human-AI cooperation by deciding when an AI system should act autonomously or defer to a human expert. Existing L2D methods, however, assume static human performance, contradicting well-established findings on fatigue-induced degradation. We propose Fatigue-Aware Learning to Defer via Constrained Optimisation (FALCON), which explicitly models workload-varying human performance using psychologically grounded fatigue curves. FALCON formulates L2D as a Constrained Markov Decision Process (CMDP) whose state includes both task features and cumulative human workload, and optimises accuracy under human-AI cooperation budgets via PPO-Lagrangian training. We further introduce FA-L2D, a benchmark that systematically varies fatigue dynamics from near-static to rapidly degrading regimes. Experiments across multiple datasets show that FALCON consistently outperforms state-of-the-art L2D methods across coverage levels, generalises zero-shot to unseen experts with different fatigue patterns, and demonstrates the advantage of adaptive human-AI collaboration over AI-only or human-onl...