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English(EN) Multilevel Fair Allocation under Additive Preferences

为层级资源分配提出新的公平概念

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员为具有层级代理关系的多层资源分配问题开发了基于嫉妒的公平概念的新变体。该研究提出了三种变体并证明了它们的有效性各不相同。研究证明，在相同偏好下，这些变体概念会收敛，并由加权轮询（MWRR）的多层扩展保证。然而，对于一般偏好，MWRR对所有概念的保证并不一致，尽管实验结果表明它仍然表现良好。 AI

影响 为层级系统中的公平资源分配引入了新颖的理论框架，可能影响未来AI代理的协调。

排序理由 该集群包含一篇详细介绍资源分配算法新理论贡献的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

为层级资源分配提出新的公平概念

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该集群包含一篇详细介绍资源分配算法新理论贡献的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Maxime Lucet, Nawal Benabbou, Aur\'elie Beynier, Nicolas Maudet ·

    多层公平分配在加性偏好下

    arXiv:2608.24400v1 Announce Type: cross Abstract: We study multilevel fair resource allocation with tree-structured hierarchical relations among agents. At each level, the problem can be viewed locally as allocating an agent's bundle to its children, the overall allocation being …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Nicolas Maudet ·

    多层公平分配在加性偏好下

    We study multilevel fair resource allocation with tree-structured hierarchical relations among agents. At each level, the problem can be viewed locally as allocating an agent's bundle to its children, the overall allocation being a trace of this process iterated down to the leave…

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