Supporting Rule-Based Control with a Natural Language Model

The usage of Artificial Intelligence (AI) in control loops and rule-based frameworks is a novel approach in automation and decision-making processes. Large Language Models (LLMs) are redefining conventional rule-based systems by introducing intuitive natural language interfaces, drastically changing the creation of rules, and minimizing operational complexity. Unlike static controllers, AI-enhanced systems can autonomously evolve with real-time environmental changes, achieving optimal performance without manual intervention. By allowing non-experts to modify rules through natural language commands, LLM can change the control system management. These advancements not only improve adaptability and operational efficiency but also reduce downtime through proactive error detection and self-correction mechanisms. AI-powered systems allow refining operations, thus accelerating response speeds and increasing reliability. The synergy between rule-based logic and AI-driven intelligence provides a new approach for autonomous systems, improving their capability of context-specific decision-making. In this paper, an approach is presented to control a storage system by natural language commands. The comparison of the Hungarian and English language interpretations is discussed.