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English(EN) MAPF-World: Action World Model for Multi-Agent Path Finding

新研究探索先进的多智能体路径寻找技术 · 已追踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了多智能体路径寻找 (MAPF) 的进展。第一篇“MAPF-World”介绍了一种自回归动作世界模型，该模型统一了短视预测和动作生成，提高了高密度环境中的决策制定和泛化能力。第二篇论文“经典多智能体路径寻找的成本”研究了经典 MAPF 公式化的局限性，并证明了连续时间 MAPF (MAPF$_R$) 可以产生质量显著更高的解决方案，尤其是在开放空间地图上。 AI

影响 这些论文推动了多智能体路径寻找的最新技术，有望提高物流和机器人导航效率。

排序理由 该集群包含两篇详细介绍多智能体路径寻找新研究的学术论文。

在 arXiv cs.AI 阅读 →

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新研究探索先进的多智能体路径寻找技术 · 已追踪 2 个来源

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该集群包含两篇详细介绍多智能体路径寻找新研究的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhanjiang Yang, Yueming Li, Yang Shen, Meng Li, Lijun Sun ·

    MAPF-World：用于多智能体路径寻找的动作世界模型

    arXiv:2508.12087v3 Announce Type: replace Abstract: Multi-agent path finding (MAPF) studies the problem of planning conflict-free paths for multiple agents from given start locations to designated goals, with applications in robot-assisted logistics and social navigation. Recent …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Martin Fabian ·

    经典多智能体路径寻找的成本

    Multi-Agent Path Finding (MAPF) is the problem of planning conflict-free paths for multiple agents in a shared space, each from its start to its goal. Classical MAPF has been the dominant formulation for many years, with its assumptions of discrete time and graph-based conflicts …

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