大規模言語モデルエージェントによる産業オートメーションシステムの制御
Control Industrial Automation System with Large Language Model Agents
産業オートメーションシステムを大規模言語モデル（LLM）でエンドツーエンドに制御するフレームワークを提案し、自然言語による操作と適応性の向上を実現した。
著者: Yuchen Xia, Nasser Jazdi, Jize Zhang, Chaitanya Shah, Michael Weyrich
分類: eess.SY, cs.AI, cs.HC, cs.MA, cs.RO, cs.SY
原文アブストラクト
Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.
