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English(EN) A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming

新算法LinMatch通过线性匹配优化人机协作

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了LinMatch，一种用于在线多人类多机器人协作的新型算法。该方法将机器人分配给人类代理的任务视为一个线性匹配赌徒问题。LinMatch在每一轮中使用匈牙利算法有效地解决了乐观匹配问题，并建立了$\tilde{\Theta}(d\sqrt{MKT})$的紧密最优遗憾率。该算法的应用范围不仅限于人机交互，还扩展到推荐系统和住房分配等领域。 AI

影响 优化了多代理协调，有可能提高复杂机器人和推荐系统的效率。

排序理由 详细介绍新算法及其理论边界的学术论文。[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 →

新算法LinMatch通过线性匹配优化人机协作

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详细介绍新算法及其理论边界的学术论文。[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) · Yaohui Guo, X. Jessie Yang, Cong Shi ·

    一种线性匹配赌博机方法用于在线多人类多机器人协同

    arXiv:2606.29221v1 Announce Type: new Abstract: We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human agents over mu…

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