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English(EN) Offline Apprenticeship Learning for Human-Centric Environments

论文推进面向人类的AI的离线学徒学习

作者 PulseAugur 编辑部 · [1 个来源] ·

本论文介绍了为智能辅导系统（ITS）和临床医疗等以人为本的环境设计的离线学徒学习（AL）新框架。所提出的方法，包括EM-EDM和THEMES，通过根据潜在奖励函数对演示进行聚类并将轨迹划分为连贯的子轨迹来专注于改进奖励建模。HALIDE和CCHAL的进一步进展通过整合连续质量信号并将质量归因于个体决策来解决演示质量问题，在各种数据集上优于现有基线。StuEDM的真实课堂评估表明学习收益显著，并保留了学生的自主性。 AI

影响 将离线AL确立为教育和医疗领域AI的一种实用、数据高效且符合伦理的范式。

排序理由 该条目是arXiv预印本，详细介绍了关于离线学徒学习框架的学术研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

论文推进面向人类的AI的离线学徒学习

本文如何被排名

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该条目是arXiv预印本，详细介绍了关于离线学徒学习框架的学术研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Md Mirajul Islam ·

    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) …

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