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Resilient Supervisory Multiagent Systems
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DOI：10.1109/TRO.2021.3108074.png)
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Abstract
En 中文
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.
Keywords:
Robot kinematics
Resilience
Task analysis
Multi-agent systems
Reinforcement learning
Security
Picture archiving and communication systems
Learning and adaptive systems
multi-robot systems
networked robots
resilience
Journal
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10.5
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3.3K
Citations:
2.8W
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