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dc.contributor.authorYasin Yazicioǧlu, A.
dc.contributor.authorEgerstedt, Magnus
dc.contributor.authorShamma, Jeff S.
dc.date.accessioned2017-01-02T09:55:29Z
dc.date.available2017-01-02T09:55:29Z
dc.date.issued2015-11-25
dc.identifier.citationYazicioglu AY, Egerstedt M, Shamma JS (2015) Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs. IEEE Transactions on Network Science and Engineering 2: 139–151. Available: http://dx.doi.org/10.1109/TNSE.2015.2503983.
dc.identifier.issn2327-4697
dc.identifier.doi10.1109/TNSE.2015.2503983
dc.identifier.urihttp://hdl.handle.net/10754/622550
dc.description.abstractMulti-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 ϵ [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. © 2015 IEEE.
dc.description.sponsorshipThis work was supported by AFOSR/MURI #FA9550-10-1-0573 and ONR Project #N00014-09-1-0751.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7337422/
dc.subjectMulti-Agent systems
dc.subjectrobust networks
dc.subjectself-organization
dc.titleFormation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Network Science and Engineering
dc.contributor.institutionLaboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, United States
dc.contributor.institutionSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
dc.identifier.arxividarXiv:1503.08131
kaust.personShamma, Jeff S.
dc.date.published-online2015-11-25
dc.date.published-print2015-10-01


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