2025
doi: 10.12720/jait.16.8.1178-1186
Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach
Paula Stocco
,
Alan Hesu ,
Steven J. Spencer*
- Unmanned Systems and Autonomy, Sandia National Laboratories, Albuquerque, USA
- Manuscript receivedFebruary 15, 2025
- revisedFebruary 25, 2025
- acceptedMay 9, 2025
- publishedAugust 26, 2025
Abstract
Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.
Keywords
- multi-agent system
- connectivity maintenance
- minimum spanning tree
- graph neural network
- evolutionary algorithm
How to Cite
Paula Stocco, Alan Hesu, and Steven J. Spencer, "Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach," Journal of Advances in Information Technology, vol. 16, no. 8, pp. 1178-1186, 2025. https://doi.org/10.12720/jait.16.8.1178-1186
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
