Fatigue-Aware Learning to Defer via Constrained Optimisation explores A fatigue-aware learning to defer framework that models workload-varying human performance for optimized human-AI cooperation.. Commercial viability score: 7/10 in Human-AI Collaboration.
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/buildability/fatigue-aware-learning-to-defer-via-constrained-optimisation
Subject: Fatigue-Aware Learning to Defer via Constrained Optimisation
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. human despite mild fatigue accumulation. At the final time step t = T, severe human fatigue leads to AI handling the
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Receipt path
/buildability/fatigue-aware-learning-to-defer-via-constrained-optimisation
Paper ref
fatigue-aware-learning-to-defer-via-constrained-optimisation
arXiv id
2604.00904
Generated at
2026-04-02T20:57:54.709Z
Evidence freshness
stale
Last verification
2026-04-02T20:57:54.709Z
Sources
3
References
69
Coverage
50%
Lineage hash
a0040c929f6929dc484cce947cd94cdddf3cdf9bb593053b922a744695ad4174
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unsigned_external
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69 refs / 3 sources / Verification pending
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This equation captures one of the core mathematical components of the system. d-dimensional input sample, and yi ∈Y ⊂{0, 1}K is the corresponding ground truth label. An AI classifier is denoted
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This equation captures one of the core mathematical components of the system. with ci : S × A × S →R. The training objective is then defined as maxπ∈C Jr(π)
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