Resiliency Analysis of LLM generated models for Industrial Automation

Oluwatosin Ogundare; Gustavo Quiros Araya; Ioannis Akrotirianakis; Ankit Shukla

doi:10.1109/ICMERALDA60125.2023.10458189

Resiliency Analysis of LLM generated models for Industrial Automation

Oluwatosin Ogundare
, Gustavo Quiros Araya
, Ioannis Akrotirianakis
, Ankit Shukla

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings
Subtitle of host publication	ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	113-116
Number of pages	4
ISBN (Electronic)	9798350369359
DOIs	https://doi.org/10.1109/ICMERALDA60125.2023.10458189
State	Published - 2023
Externally published	Yes
Event	2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023 - Virtual, Online, Indonesia Duration: Nov 24 2023 → Nov 24 2023

Publication series

Name	Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications

Conference

Conference	2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023
Country/Territory	Indonesia
City	Virtual, Online
Period	11/24/23 → 11/24/23

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Modeling and Simulation

Access to Document

10.1109/ICMERALDA60125.2023.10458189

Cite this

Ogundare, O., Araya, G. Q., Akrotirianakis, I., & Shukla, A. (2023). Resiliency Analysis of LLM generated models for Industrial Automation. In Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications (pp. 113-116). (Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMERALDA60125.2023.10458189

Ogundare, Oluwatosin ; Araya, Gustavo Quiros ; Akrotirianakis, Ioannis et al. / Resiliency Analysis of LLM generated models for Industrial Automation. Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 113-116 (Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications).

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title = "Resiliency Analysis of LLM generated models for Industrial Automation",

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.",

author = "Oluwatosin Ogundare and Araya, \{Gustavo Quiros\} and Ioannis Akrotirianakis and Ankit Shukla",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023 ; Conference date: 24-11-2023 Through 24-11-2023",

year = "2023",

doi = "10.1109/ICMERALDA60125.2023.10458189",

language = "English (US)",

series = "Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "113--116",

booktitle = "Proceedings",

address = "United States",

}

Ogundare, O, Araya, GQ, Akrotirianakis, I & Shukla, A 2023, Resiliency Analysis of LLM generated models for Industrial Automation. in Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications. Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, Institute of Electrical and Electronics Engineers Inc., pp. 113-116, 2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023, Virtual, Online, Indonesia, 11/24/23. https://doi.org/10.1109/ICMERALDA60125.2023.10458189

Resiliency Analysis of LLM generated models for Industrial Automation. / Ogundare, Oluwatosin; Araya, Gustavo Quiros; Akrotirianakis, Ioannis et al.
Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications. Institute of Electrical and Electronics Engineers Inc., 2023. p. 113-116 (Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Resiliency Analysis of LLM generated models for Industrial Automation

AU - Ogundare, Oluwatosin

AU - Araya, Gustavo Quiros

AU - Akrotirianakis, Ioannis

AU - Shukla, Ankit

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85180571187

UR - https://www.scopus.com/pages/publications/85180571187#tab=citedBy

U2 - 10.1109/ICMERALDA60125.2023.10458189

DO - 10.1109/ICMERALDA60125.2023.10458189

M3 - Conference contribution

AN - SCOPUS:85180571187

T3 - Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications

SP - 113

EP - 116

BT - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

Y2 - 24 November 2023 through 24 November 2023

ER -

Ogundare O, Araya GQ, Akrotirianakis I, Shukla A. Resiliency Analysis of LLM generated models for Industrial Automation. In Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications. Institute of Electrical and Electronics Engineers Inc. 2023. p. 113-116. (Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications). doi: 10.1109/ICMERALDA60125.2023.10458189

Resiliency Analysis of LLM generated models for Industrial Automation

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