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English(EN) Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO

新的STL-GO方法应对复杂的多智能体规划挑战

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了两种新方法，一种基于混合整数规划（MIP），另一种基于可满足性模理论（SMT），以解决具有复杂时空和拓扑约束的多智能体规划问题。这些方法利用一种称为带图算子的时空逻辑（STL-GO）的形式化来管理复杂的智能体交互和动态图拓扑。这些编码的有效性在多UAV搜救基准测试中得到了证明，展示了它们处理具有不同团队规模和图复杂性的动态多图交互的能力。 AI

影响 这些方法可以提高复杂环境（如搜救行动）中多智能体系统的协调性和效率。

排序理由 该集群包含一篇详细介绍多智能体规划新方法的论文。

在 arXiv cs.AI 阅读 →

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新的STL-GO方法应对复杂的多智能体规划挑战

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sheryl Paul, Vidisha Kudalkar, Anand Balakrishnan, Lars Lindemann, Alberto Speranzon, Jyotirmoy V. Deshmukh ·

    使用STL-GO进行具有时空和拓扑约束的多智能体规划

    arXiv:2607.28679v1 Announce Type: new Abstract: Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories. A particular challenge is the existence of spatio-temporal (i.e., when …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jyotirmoy V. Deshmukh ·

    使用STL-GO进行具有时空和拓扑约束的多智能体规划

    Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories. A particular challenge is the existence of spatio-temporal (i.e., when and/or where an agent should do what) and topolo…

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