Identification of locally influential agents in self-organizing multi-agent systems
Identification of locally influential agents in self-organizing multi-agent systems
复制标题
自组织多智能体系统中局部影响力智能体的识别
DOI：
10.1109/acc.2015.7170758
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
S. Brennan
中科院分区：
文献类型：
--
作者：
Kshitij Jerath;S. Brennan
目前的研究方法针对测量特定的代理商对一个大规模的多智能体系统（MAS）的动力学的影响，在很大程度上依赖于全阶系统的可控性的概念，或通过用户定义的宏观系统属性的代理动力学的比较。然而，众所周知，几个大规模的多智能体系统往往是自组织的，它们的动力学往往驻留在一个低维流形上。建议的框架使用这一事实来衡量代理的宏观动态的影响。首先，可以封装的低维流形与自组织动力学的最小嵌入维数确定使用修改的方法的假邻居。其次，使用Krylov子空间模型降阶技术将全阶动力学投影到局部低维流形上。最后，一个现有的基于能力的度量应用到本地降阶表示，以衡量代理的自组织动态的影响。有了这种技术，人们可以确定区域的状态空间中的代理具有显着的局部影响的自组织MAS的动态。所提出的技术证明，将其应用到交通中的车辆集群形成的问题，一个典型的自组织系统。因此，现在可以识别道路的区域，在这些区域中，单个驾驶员有能力影响自组织交通拥堵的动态。
Current research methods directed towards measuring the influence of specific agents on the dynamics of a large-scale multi-agent system (MAS) rely largely on the notion of controllability of the full-order system, or on the comparison of agent dynamics via a user-defined macroscopic system property. However, it is known that several large-scale multi-agent systems tend to self-organize, and their dynamics often reside on a low-dimensional manifold. The proposed framework uses this fact to measure an agent's influence on the macroscopic dynamics. First, the minimum embedding dimension that can encapsulate the low-dimensional manifold associated with the self-organized dynamics is identified using a modification of the method of false neighbors. Second, the full-order dynamics are projected onto the local low-dimensional manifold using Krylov subspace-inspired model order reduction techniques. Finally, an existing controllability-based metric is applied to the local reduced-order representation to measure an agent's influence on the self-organized dynamics. With this technique, one can identify regions of the state space where an agent has significant local influence on the dynamics of the self-organizing MAS. The proposed technique is demonstrated by applying it to the problem of vehicle cluster formation in traffic, a prototypical self-organizing system. As a result, it is now possible to identify regions of the roadway where an individual driver has the ability to influence the dynamics of a self-organized traffic jam.
线性系统的克雷洛夫子空间方法：算法原理
DOI：
--
发表时间：
2023
期刊：
影响因子：
--
作者：
Matsumoto;J.;H. Kubota;T. Inoue;I. Akasaka;H. Kamahori;F. Fujibe;T. Hayashi;T. Terao;F. Murata;H. Fujinami;A.;T. Sogabe
通讯作者：
T. Sogabe