Enhancing Holonic Architecture with Natural Language Processing for System of Systems

Abstract

The ever-growing complexity and dynamic nature of modern System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems (CS), referred to as holons in the holonic architecture of SoS. This paper proposes a novel approach to enhance humand-to-holon and holon-to-holon communication within the holonic architecture through the integration of Natural Language Processing (NLP) techniques. Our proposed framework utilizes advancements in NLP, specifically Large Language Models (LLMs), enabling holons to understand and act on natural language instructions. This enables more intuitive holon-to-holon and human-to-holon interactions, leading to better coordination among diverse systems. The framework’s practical application is demonstrated through an Unmanned Vehicle Fleet (UVF) case study, showcasing its potential in enhancing communication and coordination in complex SoS. Additionally, we propose evaluat ion strategies to assess the efficiency and effectiveness of this framework, and identify areas for improvement. This work sets the stage for future exploration and prototype implementation, paving the way for further advancements in SoS communication and collaboration.