SMAHI-ER: Symbiotic Multi-Agent Hybrid Intelligence for Resilient Human-Machine Manufacturing
Febin K Renu
IEEE IEHNS 2026
Abstract
SMAHI-ER is a human-centric Industry 5.0 framework that models the manufacturing ecosystem as a dynamic hypergraph, joining Human Digital Twins, Machine Cognitive Twins and Ethical Meta-Agents through relationships of trust, communication, dependency and ethical alignment. Graph neural networks give the system emergent cognition; generative diffusion reinforcement learning synthesises adaptive strategies; causal SHAP keeps its ethical decisions interpretable; and federated learning lets factories collaborate without surrendering their data. Evaluated across more than 50 disruption scenarios with 30 Monte Carlo runs each, it sustains 88.1% overall equipment effectiveness, a 1.2% defect rate and 91.5% control of worker stress, with 2.15× the emergent intelligence of baseline approaches.
- Industry 5.0
- multi-agent systems
- hypergraph GNN
- diffusion RL
- causal SHAP
- federated learning
- digital twins
BibTeX
@inproceedings{renu2026smahier,
title = {SMAHI-ER: Symbiotic Multi-Agent Hybrid Intelligence for Resilient Human-Machine Manufacturing},
author = {Febin K Renu},
booktitle = {IEEE IEHNS 2026},
year = {2026},
doi = {10.1109/IEHNS68708.2026.11607072},
}