Resilient Supervisory Multiagent Systems

Journal Title
Journal ISSN
Volume Title
IEEE Transactions on Robotics
Accidental or deliberate disruption of the coordination function in a multiagent system has been discussed and referred to in the social sciences literature as leader decapitation; this article outlines a methodology for making multiagent networks resilient to this type of failure, enabling a timely restoration of operation normalcy by leveraging machine learning techniques. The approach involves endowing the agents with a cascade of independent learning modules that enable them to discover over time their role in the overall system coordinating strategy, so that they are able to autonomously implement it when central coordination seizes to function. Through these machine learning algorithms, the agents incrementally identify the overall system’s task specification and simultaneously optimize their strategy to serve the common goal.
This article was originally published in IEEE Transactions on Robotics. The version of record is available at: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Learning and adaptive systems, multi-robot systems, networked robots, resilience
Baxevani, Kleio, Ashkan Zehfroosh, and Herbert G. Tanner. 2021. “Resilient Supervisory Multiagent Systems.” IEEE Transactions on Robotics, 1–15.