Resiliency Analysis of LLM generated models for Industrial Automation
Oluwatosin Ogundare, Gustavo Quiros Araya, Ioannis Akrotirianakis,, Ankit Shukla
TL;DR
This paper investigates the resilience and efficiency of industrial automation systems generated by Large Language Models, using percolation theory and stochastic optimization to evaluate and improve their reliability.
Contribution
It introduces a novel framework combining percolation theory and stochastic optimization to analyze and enhance LLM-generated industrial automation systems.
Findings
Resilience estimates derived from percolation theory.
Near-optimal solutions with provable regret bounds.
Insights into system reliability and areas for improvement.
Abstract
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.
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Taxonomy
TopicsFlexible and Reconfigurable Manufacturing Systems · Fault Detection and Control Systems · Manufacturing Process and Optimization