等连通拓扑在多智能体交互中的作用
Role of Iso-connectivity Topologies in Multi-agent Interactions
AI总结：
本文探讨了等连通拓扑在多智能体系统动态中的作用，通过分析不同拓扑结构对信息共享能力的影响，提出了解决多智能体图结构确定问题的方法。
摘要：
本文提出了一种探索具有相同连通性度量的不同拓扑结构（即等连通拓扑）在多智能体系统动态中的益处。多智能体网络中智能体之间的全局信息共享能力可通过称为关联图的代数连通性来量化。具有相同连通性的不同拓扑在多智能体动态中起着重要作用，因为它们能根据合作任务的要求重新组织智能体的位置，而不会牺牲整个网络的信息交换能力。确定具有相同连通性的不同多智能体图是一个多模态问题，即存在多个共享相同连通性的图。本文提出了确定具有相同连通性的不同图的解析解。通过适当的相似变换，发现了一组等谱图。此外，揭示了密集图中一个无连通性变化的区域，其中智能体可以自由移动而不改变全局连通性。所提出的方法通过充分的例子进行了验证。
英文摘要：
In this paper, we present the benefits of exploring different topologies with equal connectivity measure, or iso-connectivity topologies, in relation to the multiagent system dynamics. The level of global information sharing ability among agents in a multi-agent network can be quantified by a connectivity measure, called as the Algebraic Connectivity of the associated graph consisting of point-mass agents as nodes and inter-connection links between them as edges. Distinct topologies with the same connectivity play profound role in multi-agent dynamics as they offer various ways to reorganize agents locations according to the requirement during a cooperative mission, without sacrificing the information exchange capability of the entire network. Determination of the distinct multi-agent graphs with identical connectivity is a multimodal problem, in other words, there exist multiple graphs that share the same connectivity. We present analytical solutions of determining distinct graphs with identical connectivity. A family of isospectral graphs are found out by utilizing an appropriate similarity transformation. Moreover, a zone of no connectivity change in a dense graph is unraveled where an agent can move freely without causing any change in the global connectivity. The proposed solutions are validated with the help of sufficient examples.
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。
