Resiliency Analysis of LLM generated models for Industrial Automation

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Ogundare, Oluwatosin, Gustavo Quiros Araya, Akrotirianakis, Ioannis, Shukla, Ankit
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.08.2023
Subjects
Automation
Control systems
Design optimization
Large language models
Percolation theory
Reliability analysis
Resilience
System effectiveness
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Summary: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.
ISSN:2331-8422