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Deutsch(DE) Belief-Aware Multi-Agent Path Finding under Map Uncertainty

新的MAGIC框架改进了地图不确定性下的多智能体路径寻找

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为MAGIC（Multi-Agent Gaussian belief Inference for Coordination）的新框架，以解决多智能体路径寻找（MAPF）中的不确定性问题。该系统基于智能体的观测，实时更新关于可通行性的共享信念，并利用空间相关性来推断未观测区域的可通行性。实验表明，与现有方法相比，MAGIC显著降低了执行成本，即使对于多达800个智能体的大型团队也证明是有效的。 AI

影响 增强了在不确定环境中的机器人协调和导航能力，有望提高物流和自主系统的效率。

排序理由 该集群包含一篇详细介绍多智能体路径寻找新算法的研究论文。

在 arXiv cs.AI 阅读 →

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新的MAGIC框架改进了地图不确定性下的多智能体路径寻找

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报道来源 [2]

  1. arXiv cs.AI TIER_1 Deutsch(DE) · Viraj Parimi, Shao-Hung Chan, Han Zhang, Jingkai Chen, Brian Williams ·

    地图不确定性下的信念感知多智能体路径寻找

    arXiv:2609.40269v1 Announce Type: new Abstract: Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedl…

  2. arXiv cs.MA (Multiagent) TIER_1 Deutsch(DE) · Brian Williams ·

    地图不确定性下的信念感知多智能体路径寻找

    Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedly due to fallen objects, spills, or other local …

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