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

Authors

D. Hinkelmann, A. Schmeink, G. Dartmann,

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.

BibTEX Reference Entry 

@inproceedings{HiScDa18,
	author = {Daniel Hinkelmann and Anke Schmeink and Guido Dartmann},
	title = "Distributed learning-based state prediction for multi-agent systems with reduced communication effort",
	pages = "1-5",
	booktitle = "Workshop on Sensor Data Fusion and Machine Learning for next Generation of Cyber-Physical-Systems (SeFuMal) in conjunction with ACM International Conference on Computing Frontiers 2018",
	address = {Ischia, Italy},
	month = May,
	year = 2018,
	hsb = RWTH-2018-224866,
	}

Downloads

 Download paper  Download bibtex-file

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights there in are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.