标题： 分布鲁棒的多智能体会合级联风险量化：时间延迟和网络连通性的影响 显示英文标题

标题： Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity

摘要： 在自主多智能体系统中实现安全性，尤其是在时间关键任务如会合中，是一个关键挑战。 在本文中，我们提出了一种分布鲁棒风险框架，用于分析多智能体会合中的级联故障。 为了捕捉网络连通性、系统动力学和通信延迟之间的复杂相互作用，我们使用了一个带有时间延迟的网络模型作为基准。 我们引入了一个条件分布鲁棒函数，利用双变量正态分布来量化智能体之间的级联效应。 我们的方法得出了闭式风险表达式，揭示了时间延迟、噪声统计特性、通信拓扑和故障模式对会合风险的影响。 得出的见解有助于设计能够缓解级联故障风险的弹性网络。 我们通过大量仿真验证了理论结果，展示了我们框架的有效性。 显示英文摘要

摘要： Achieving safety in autonomous multi-agent systems, particularly in time-critical tasks like rendezvous, is a critical challenge. In this paper, we propose a distributionally robust risk framework for analyzing cascading failures in multi-agent rendezvous. To capture the complex interactions between network connectivity, system dynamics, and communication delays, we use a time-delayed network model as a benchmark. We introduce a conditional distributionally robust functional to quantify cascading effects between agents, utilizing a bi-variate normal distribution. Our approach yields closed-form risk expressions that reveal the impact of time delay, noise statistics, communication topology, and failure modes on rendezvous risk. The insights derived inform the design of resilient networks that mitigate the risk of cascading failures. We validate our theoretical results through extensive simulations, demonstrating the effectiveness of our framework.