This paper proposes a study of the resilience and efficiency of automatically generated industrial automation and control systems using Large Language Models (LLMs). The approach involves modeling the system using percolation theory to estimate its resilience and formulating the design problem as an optimization problem subject to constraints. Techniques from stochastic optimization and regret analysis are used to find a near-optimal solution with provable regret bounds. The study aims to provide insights into the effectiveness and reliability of automatically generated systems in industrial automation and control, and to identify potential areas for improvement in their design and implementation.

翻译：本文提出了一项关于使用大语言模型（LLMs）自动生成的工业自动化与控制系统鲁棒性及效率的研究。该方法利用渗透理论对系统进行建模以评估其鲁棒性，并将设计问题表述为带约束的优化问题。借助随机优化与遗憾分析技术，可找到具有可证明遗憾边界的近优解。本研究旨在揭示工业自动化与控制领域中自动生成系统的有效性与可靠性，并识别其设计与实现中潜在的改进方向。