Work

Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

Year: 2015

Type: article

Abstract: Multi-agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m E [k; k + 2]. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges.

Source: IEEE Transactions on Network Science and Engineering

DOI: 10.1109/tnse.2015.2503983

Authors: A. Yasin Yazicioglu, Magnus Egerstedt, Jeff S. Shamma

Institutions: Georgia Institute of Technology, King Abdullah University of Science and Technology

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Multidisciplinary University Research Initiative, Office of Naval Research, Air Force Office of Scientific Research