社交动态系统的弹性监控：基于延迟环境下多智能体协作网络
Resilient Monitoring of Social Dynamical Systems through Collaborative Multi-Agent Networks under Latency
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中文总结 AI 辅助
本文提出一种单时间尺度分布式推理模型，用于在延迟和智能体故障下监控社交动态网络，并开发了基于图论的故障恢复机制，确保系统稳定性与可观测性。
中文摘要 AI 辅助
社交动态网络显著影响当代数字格局，其影响范围从社会活动到公共政策制定。本文研究使用多智能体系统（MAS）来监控和分析这些网络。首先，我们提出一种单时间尺度分布式推理模型，旨在有效应对延迟和智能体故障等挑战。其次，我们提供了确保所提方案稳定性的充分条件。值得注意的是，观测器增益设计不受时间延迟的影响。第三，我们开发了一种计算高效的智能体故障恢复机制，该机制利用图论方法，通过替换故障智能体（由于感知故障或无界延迟，即数据包丢失）来恢复网络可观测性，具体采用计算高效的图论方法指派观测等效的智能体对应物。最后，我们通过一个教学示例和实际网络应用展示了所提方案。
英文摘要
Social dynamical networks significantly influence contemporary digital landscapes, affecting realms from social activism to public policy formulation. This paper investigates the use of multi-agent systems (MAS) to monitor and analyze these networks. Firstly, we propose a single-time-scale distributed inference model designed to effectively manage challenges such as latency and agent failure. Secondly, we provide sufficient conditions that ensure the stability of the proposed scheme. Notably, the observer gain design remains effective regardless of time delays. Thirdly, we develop a computationally efficient recovery mechanism for agent failures that relies on employing graph-theoretic approaches to restore network observability by replacing failed agents (due to sensing failure or unbounded delays, i.e., packet drops) by implementing computationally efficient graph-theoretic methods to assign observationally equivalent agent counterparts. Lastly, we illustrate the proposed scheme through a pedagogical example and real-world network applications.
发表机构
- Semnan University（塞姆南大学）
- Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）
- Institute for Systems and Robotics, Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院系统机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。
