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arXiv 2004.06533cs.MAmath.DS
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开放多智能体系统中平均一致性的基本性能限制

Fundamental Performance Limitations for Average Consensus in Open Multi-Agent Systems

Charles Monnoyer de Galland, Julien M. Hendrickx

摘要:

我们得到基本的性能限制内在的平均共识问题,在开放的多智能体系统,这是系统受到频繁的到达和离开的代理。每个代理持有一个值,并且代理的目标是协作地估计系统中当前代理的值的平均值。在开放系统中解决这些问题的算法永远不会收敛,因为系统代理所追求的组成,大小和目标的永久变化。我们提供的预期均方误差的下限可实现的任何平均算法在开放系统的固定大小。我们的推导是基于分析的概念算法,将实现最佳性能为一个给定的模型的替代品。我们得到一个一般的约束,依赖于定义代理之间的相互作用的模型的属性,并实例化的结果为所有对一和一对一的交互模型。这些边界和算法实现与这些模型之间的比较,然后提供突出其有效性。

英文摘要:

We derive fundamental performance limitations for intrinsic average consensus problems in open multi-agent systems, which are systems subject to frequent arrivals and departures of agents. Each agent holds a value, and the objective of the agents is to collaboratively estimate the average of the values of the agents presently in the system. Algorithms solving such problems in open systems are poised to never converge because of the permanent variations in the composition, size and objective pursued by the agents of the system. We provide lower bounds on the expected Mean Squared Error achievable by any averaging algorithms in open systems of fixed size. Our derivation is based on the analysis of a conceptual algorithm that would achieve optimal performance for a given model of replacements. We obtain a general bound that depends on the properties of the model defining the interactions between the agents, and instantiate that result for all-to-one and one-to-one interaction models. A comparison between those bounds and algorithms implementable with those models is then provided to highlight their validity.

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。

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