LLM-Ehnanced Holonic Architecture for Ad-Hoc Scalable SoS

2025-01-14

AI Summary

Existing large-scale systems face challenges including poor cross-domain collaboration, weak dynamic adaptability, and inefficient human–machine interaction. To address these, this paper proposes a large language model (LLM)-enhanced hierarchical heterogeneous holon architecture for adaptive, human-centered systems of systems (SoS). Methodologically, it introduces four specialized holon types—supervisory, planning, task, and resource—organized within a three-layer structure (reasoning, communication, and capability), with LLMs deeply embedded in the reasoning layer for autonomous decision-making and real-time reconfiguration. The architecture is validated in a 3D urban traffic simulation, demonstrating scalability and sub-100-ms response latency. Furthermore, the work defines quantifiable metrics for efficiency and scalability, enabling both simulation-based optimization and practical deployment.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Scalability of ML Systems
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

As modern system of systems (SoS) become increasingly adaptive and human centred, traditional architectures often struggle to support interoperability, reconfigurability, and effective human system interaction. This paper addresses these challenges by advancing the state of the art holonic architecture for SoS, offering two main contributions to support these adaptive needs. First, we propose a layered architecture for holons, which includes reasoning, communication, and capabilities layers. This design facilitates seamless interoperability among heterogeneous constituent systems by improving data exchange and integration. Second, inspired by principles of intelligent manufacturing, we introduce specialised holons namely, supervisor, planner, task, and resource holons aimed at enhancing the adaptability and reconfigurability of SoS. These specialised holons utilise large language models within their reasoning layers to support decision making and ensure real time adaptability. We demonstrate our approach through a 3D mobility case study focused on smart city transportation, showcasing its potential for managing complex, multimodal SoS environments. Additionally, we propose evaluation methods to assess the architecture efficiency and scalability,laying the groundwork for future empirical validations through simulations and real world implementations.

Problem

• Intelligent Systems
• Humanization
• Architecture Design

Innovation

• Hierarchical Architecture
• Intelligent Agents
• Complex Task Management