Offline Apprenticeship Learning for Human-Centric Environments

arXiv:2610.28716v1 Announce Type: new Abstract: Effective decision-making in human-centric environments such as Intelligent Tutoring Systems (ITSs) and clinical healthcare requires policies that are adaptive, data-efficient, and ethically deployable. Apprenticeship Learning (AL) infers policies directly from expert demonstrations by recovering the underlying reward structure, enabling strictly offline learning without environment interaction. This dissertation develops a progression of offline AL frameworks organized around two challenges: reward modeling and demonstration quality. For reward modeling, EM-EDM clusters demonstrations by latent reward functions and induces cluster-specific policies, and THEMES partitions trajectories into temporally coherent sub-trajectories guided by a hierarchically learned reward regulator; across one educational and three clinical datasets, THEMES outperforms flat and hierarchical AL baselines. For demonstration quality, HALIDE integrates continuous quality signals into hierarchical policy induction, enabling principled reuse of mixed-quality data under distribution shift. CCHAL further attributes quality to individual decisions via Bayesian credit assignment, dual-arm criticality scoring that contrasts expert and non-expert behavior, and a Bradley-Terry contrastive IRL objective over failure trajectories, outperforming baselines including HALIDE on three clinical EHR datasets and an ITS dataset. Finally, a real-world classroom evaluation of StuEDM, an EDM-induced policy that preserves student agency and intervenes only on disagreement, s

Improves decision-making in human-centric environments, but raises ethical concerns.

The research advances offline apprenticeship learning to improve adaptive, data-efficient, and ethically sound decision-making in human-centric domains like education and healthcare. These improvements suggest potential benefits in human flourishing by enhancing personalized support without active environment interaction. However, deployment challenges and reliance on demonstration quality moderate the impact.

This is a dissertation summary presenting incremental technical methods for data-efficient policy learning in tutoring and clinical settings, with one real classroom evaluation showing modest learning gains. The societal implication is mildly positive but narrow, academic, and not yet broadly deployed.

This research presents a novel approach to AI policy learning in sensitive human-centric environments, demonstrating significant improvements in educational and clinical contexts. The methods emphasize ethical deployment, data efficiency, and the preservation of human agency, particularly highlighted by the StuEDM classroom evaluation. These advancements could lead to more effective and less intrusive AI applications.

The paper presents multiple frameworks that outperform baselines in real-world datasets. These frameworks enable adaptive, data-efficient, and ethically deployable policies in critical areas like healthcare and education.

SOURCE     arXiv cs.LG (Machine Learning) (tier 1)
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DOCUMENT   a5580405-06d2-4865-8157-b668cf06d9fd
           https://arxiv.org/abs/2610.28716
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +16  (Mixed or uncertain)
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CONFIDENCE 78%