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Distributed learning-based state prediction for multi-agent systems with reduced communication effort

Authors:: Hinkelmann, D. , Schmeink, A. , Dartmann, G.
Book Title:: effort Workshop on Sensor Data Fusion and Machine Learning for next Generation of Cyber-Physical-Systems in conjunction with ACM International Conference on Computing Frontiers
Publisher:: ACM New York, NY, USA
Pages:: p.p. 376-380
Date:: 2018
DOI:: 10.1145/3203217.3203230
hsb:: RWTH-2018-224866
Language:: English
Abstract:: A novel distributed event-triggered communication for multi-agent systems is presented. Each agent predicts its future states via an artificial neural network, where the prediction is solely based on own past states. The approach is therefore scalable with the number of agents. A communication is triggered if the discrepancy between actual and predicted state exceeds a threshold. Numerical results show that this approach reduces the communication effort remarkably compared to existing methods.
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