Abstract
This paper introduces Stratified Unified Coordination Networks (SUCN), a next-generation multi-agent system architecture designed for extreme-scale autonomy in heterogeneous environments. SUCN enables decentralized agents to dynamically form, dissolve, and reconfigure hierarchical coordination structures under uncertainty, partial observability, and adversarial conditions. Unlike conventional multi-agent reinforcement learning (MARL) systems that rely on static communication graphs or centralized critics, SUCN embeds self-evolving coordination grammars, latent role synthesis, and probabilistic intent alignment fields to enable emergent system-wide coherence without centralized orchestration.
I present the theoretical formulation, system architecture, learning dynamics, and convergence properties of SUCN, along with a commercialization pathway toward deployment in autonomous infrastructure, distributed cybersecurity systems, planetary-scale sensor networks, and autonomous economic agents between 2050 and 2060.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny, "Stratified Adaptive Coordination Networks (SACN): A Next-Generation Multi-Agent Paradigm for Large-Scale Autonomous Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9993