用知识图谱连接物理与数字机器人系统，实现智能协同。
KG-MAS: Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments
- 以知识图谱为共享世界模型，统一物理/数字组件语义
- 自主代理实时查询与更新知识图谱，支持动态协同
- 模型驱动架构可自动生成代理，降低系统维护成本
在工业4.0背景下的信息物理系统中，物理与数字环境的无缝集成面临系统异构性与复杂性的挑战。传统方法依赖僵化的数据驱动方案，如共仿真框架或脆弱的点对点中间件，缺乏语义丰富性与灵活性。本文提出知识图谱增强的多智能体系统（KG-MAS），通过集中式知识图谱（KG）作为动态共享世界模型，为多智能体系统（MAS）提供统一语义基础。物理与数字组件对应的自主代理通过查询该图谱进行决策，并将实时状态信息回写至图谱。系统采用模型驱动架构，支持从语义描述自动生成功能代理，简化系统扩展与维护。通过抽象底层通信协议并提供统一智能协调机制，KG-MAS为异构物理与数字机器人环境的耦合提供了鲁棒、可扩展且灵活的解决方案。
原文摘要 · Abstract (English)
The seamless integration of physical and digital environments in Cyber-Physical Systems(CPS), particularly within Industry 4.0, presents significant challenges stemming from system heterogeneity and complexity. Traditional approaches often rely on rigid, data-centric solutions like co-simulation frameworks or brittle point-to-point middleware bridges, which lack the semantic richness and flexibility required for intelligent, autonomous coordination. This report introduces the Knowledge Graph-Enhanced Multi-Agent Infrastructure(KG-MAS), as resolution in addressing such limitations. KG-MAS leverages a centralized Knowledge Graph (KG) as a dynamic, shared world model, providing a common semantic foundation for a Multi-Agent System(MAS). Autonomous agents, representing both physical and digital components, query this KG for decision-making and update it with real-time state information. The infrastructure features a model-driven architecture which facilitates the automatic generation of agents from semantic descriptions, thereby simplifying system extension and maintenance. By abstracting away underlying communication protocols and providing a unified, intelligent coordination mechanism, KG-MAS offers a robust, scalable, and flexible solution for coupling heterogeneous physical and digital robotic environments.
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