耦合多智能体系统中不同去中心化程度的抽象
Abstractions of Varying Decentralization Degree for Coupled Multi-Agent Systems
AI总结：
本文提出一种可调节去中心化程度的多智能体系统去中心化抽象框架，通过定义m-邻居集和空间时间离散化条件，确保抽象系统的可量化转移可能性。
摘要：
本文旨在开发一种用于受耦合约束的多智能体系统的去中心化抽象框架，具有可变的去中心化程度。方法基于我们近期工作中使用的分析，其中仅基于每个智能体邻居信息的去中心化抽象被推导出来。在本文的第一部分中，我们定义了每个智能体的m-邻居集，这构成了所用去中心化程度的度量。然后提供了关于空间和时间离散化的充分条件，以提供抽象系统的模型，从而保证提取具有可量化转移可能性的转移系统。
英文摘要：
In this report, we aim at the development of a decentralized abstraction framework for multi-agent systems under coupled constraints, with the possibility for a varying degree of decentralization. The methodology is based on the analysis employed in our recent work, where decentralized abstractions based exclusively on the information of each agent's neighbors were derived. In the first part of this report, we define the notion each agent's m-neighbor set, which constitutes a measure for the employed degree of decentralization. Then, sufficient conditions are provided on the space and time discretization that provides the abstract system's model, which guarantee the extraction of a transition system with quantifiable transition possibilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
