Can LLMs improve self-adapting holonic systems?
LLM-Enhanced Holonic Architecture for Self-Adaptive System of Systems
This paper proposes an enhanced holonic architecture for Systems of Systems (SoS), using Large Language Models (LLMs) to improve adaptability and human interaction. It introduces a layered structure for individual holons (representing constituent systems) comprising reasoning (LLM-based), communication, and capability layers. Furthermore, it introduces specialized holons (supervisor, planner, task, and resource) inspired by intelligent manufacturing to enhance SoS management. The architecture is demonstrated through a 3D mobility case study involving coordinating air and ground transportation in a smart city. Key LLM aspects include natural language interfaces for human interaction, context-aware decision-making within holons, dynamic task planning, and real-time adaptation to changing conditions.