论文标题
开放多代理系统中平均共识的基本绩效限制
Fundamental Performance Limitations for Average Consensus in Open Multi-Agent Systems
论文作者
论文摘要
我们在开放的多代理系统中对固有平均共识问题产生了基本的绩效限制，该系统是经常出现和偏离代理的系统。每个代理都有一个值，代理的目的是协作估计系统中当前代理值的平均值。由于系统代理所追求的构图，大小和客观的永久变化，解决开放系统中解决此类问题的算法将永远不会融合。我们通过在固定尺寸的开放系统中的任何平均算法实现的预期平方误差提供了下限。我们的推导基于对概念算法的分析，该算法将对给定的替换模型实现最佳性能。我们获得了一个一般界限，该结合取决于定义代理之间相互作用的模型的属性，并实例化了全能和一对一的交互模型的结果。然后提供可实现的这些界限和算法之间的比较，以突出它们的有效性。
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.
