A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT

Abstract

This article introduces OPRA (Observation-Prompt-Response-Action) and its multi-agent extension, COPRA (Collaborative OPRA), as frameworks offering alternatives to traditional agent architectures in intelligent manufacturing systems. Designed for adaptive decision-making in dynamic environments, OPRA enables agents to request external knowledge—such as insights from large language models—to bridge gaps in understanding and guide optimal actions in real-time. When predefined rules or operational guidelines are absent, especially in contexts marked by uncertainty, complexity, or novelty, the OPRA framework empowers agents to query external knowledge systems (e.g., ChatGPT), supporting decisions that traditional algorithms or static rules cannot adequately address. COPRA extends this approach to multi-agent scenarios, where agents collaboratively share insights from prompt-driven responses to achieve coordinated, efficient actions. These frameworks offer enhanced flexibility and responsiveness, which are critical for complex, partially observable manufacturing tasks. By integrating real-time knowledge, they reduce the need for extensive training data and improve operational resilience, making them a promising approach to sustainable manufacturing. Our study highlights the added value OPRA provides over traditional agent architectures, particularly in its ability to adapt on-the-fly through knowledge-driven prompts and reduce complexity by relying on external expertise. Motivational scenarios are discussed to demonstrate OPRA’s potential in critical areas such as predictive maintenance.

Keywords

Intelligent sustainable manufacturing / Industry 4.0 / Industry 5.0 / Large language models / ChatGPT / Knowledge-informed machine learning / Intelligent agents / Predictive maintenance

Author Contributions

Supervision, V.T. Framework basics and conceptualization, V.T. and O.V. Experimental scenarios, O.V. and O.T. Framework schema and translators, O.T. Writing, review, editing, V.T., O.V. and O.T.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

This study did not generate or analyze any external data.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Narkhede G, Pasi B, Rajhans N, Kulkarni A. Industry 5.0 and the future of sustainable manufacturing: A systematic literature review. Bus. Strateg. Dev. 2023, 6, 704-723. doi:10.1002/bsd2.272.

[2] Kumar R, Prasad A, Kumar A. Gupta S, Singh RK, FL USA, Sustainable Smart Manufacturing Processes in Industry 4.0. In Smart Manufacturing: Concepts and Applications, 1st ed.; Eds.; CRC Press: Boca Raton, 2023; Volume 3, pp. 1-320. doi:10.1201/9781003436072.

[3] Tripathi V, Chattopadhyaya S, Mukhopadhyay AK, Sharma S, Kumar V, Li C, et al. Lean, green, and smart manufacturing: An ingenious framework for enhancing the sustainability of operations management on the shop floor in industry 4.0. J. Process Mech. Eng. 2024, 238, 1976-1990. doi:10.1177/09544089231159834.

[4] Ramakrishna S, Zhang TY, Lu WC, Qian Q, Low JSC, Yune JHR, et al. Materials informatics. J. Intell. Manuf. 2019, 30, 2307-2326. doi:10.1007/s10845-018-1392-0.

[5] Hu S, Li C, Li B, Yang M, Wang X, Gao T, et al. Digital twins enabling intelligent manufacturing: From methodology to application. Intell. Sustain. Manuf. 2024, 1, 10007. doi:10.35534/ism.2024.10007.

[6] Agrawal R, Majumdar A, Kumar A, Luthra S. Integration of artificial intelligence in sustainable manufacturing: Current status and future opportunities. Oper. Manag. Res. 2023, 16, 1720-1741. doi:10.1007/s12063-023-00383-y.

[7] Kumar R, Rani S, Khangura SS. Machine Learning for Sustainable Manufacturing in Industry 4.0: Concept, Concerns and Applications. In Sharma A, Gupta P, FL USA, Advanced Machine Learning Applications in Smart Manufacturing, 1st ed.; Eds.; CRC Press: Boca Raton, 2023; pp. 1-280. doi:10.1201/9781003453567.

[8] Guo Y, Zhang W, Qin Q, Chen K, Wei Y. Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources. Soft Comput. 2023, 27, 4061-4076. doi:10.1007/s00500-021-06593-5.

[9] An Q, Yang J, Li J, Liu G, Chen M, Li C. A state-of-the-art review on the intelligent tool holders in machining. Intell. Sustain. Manuf. 2023, 1, 10002. doi:10.35534/ism.2024.10002.

[10] Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, et al. Emergent abilities of large language models. arXiv 2022. doi:10.48550/arXiv.2206.07682.

[11] Naveed H, Khan AU, Qiu S, Saqib M, Anwar S, Usman M, et al. A comprehensive overview of large language models. arXiv 2023. doi:10.48550/arXiv.2307.06435.

[12] Dakhel AM, Nikanjam A, Khomh F, Desmarais MC, Washizaki H. An overview on large language models. In Generative AI for Effective Software Development; Springer: Cham, Switzerland, 2024; pp. 3-21. doi:10.1007/978-3-031-55642-5_1.

[13] Chang Y, Wang X, Wang J, Wu Y, Yang L, Zhu K, et al. A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. 2024, 15, 1-45. doi:10.1145/3641289.

[14] Changeux A, Montagnier S. Strategic decision-making support using large language models (LLMs). Manag. J. Adv. Res. 2024, 4, 102-108. doi:10.5281/zenodo.13444483.

[15] Shi F, Zhang Y, Qu C, Fan C, Chu J, Jin L, et al. Leveraging the power of large language models to drive progress in the manufacturing industry. In Proceedings of the 9th International Conference on Financial Innovation and Economic Development, 15-17 June 2024, San Francisco, CA, USA; Atlantis Press: Paris, France, 2024; pp. 125-133. doi:10.2991/978-94- 6463-408-2_15.

[16] Li Y, Zhao H, Jiang H, Pan Y, Liu Z, Wu Z, et al. Large language models for manufacturing. arXiv 2024. doi:10.48550/arXiv.2410.21418.

[17] Saied WM, Elakhdar BE, Hassan DG.Comprehensive synthesis of decision-making in complex systems. In Proceedings of the 6th International Conference on Computing and Informatics, 12-14 March 2024, New York, NY, USA; IEEE: New York, NY, USA, 2024; pp. 466-469. doi:10.1109/ICCI61671.2024.10485131.

[18] Grybauskas A, Cárdenas-Rubio J. Unlocking employer insights: Using large language models to explore human-centric aspects in the context of industry 5.0. Technol. Forecast. Soc. Change 2024, 208, 123719. doi:10.1016/j.techfore.2024.123719.

[19] Von Rueden L, Mayer S, Beckh K, Georgiev B, Giesselbach S, Heese R, et al. Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans. Knowl. Data Eng. 2021, 35, 614-633. doi:10.1109/TKDE.2021.3079836.

[20] Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 2019, 378, 686-707. doi:10.1016/j.jcp.2018.10.045.

[21] Terziyan V, Vitko O. Taxonomy-informed neural networks for smart manufacturing. Procedia Comput. Sci. 2024, 232, 1388-1399. doi:10.1016/j.procs.2024.01.137.

[22] Chen Y, Koohy S. GPT-PINN: Generative pre-trained physics-informed neural networks toward non-intrusive meta-learning of parametric PDEs. Finite Elem. Anal. Des. 2024, 228, 104047. doi:10.1016/j.finel.2023.104047.

[23] Antonelo EA, Camponogara E, Seman LO, Jordanou JP, de Souza ER, Hübner JF. Physics-informed neural nets for control of dynamical systems. Neurocomputing 2024, 579, 127419. doi:10.1016/j.neucom.2024.127419.

[24] Deng W, Nguyen K, Medjaher K, Gogu C, Morio J. Physics-informed machine learning in prognostics and health management: State of the art and challenges. Appl. Math. Model. 2023, 124, 325-352. doi:10.1016/j.apm.2023.07.011.

[25] Wacker JG. A definition of theory: Research guidelines for different theory-building research methods in operations management. J. Oper. Manag. 1998, 16, 361-385. doi:10.1016/S0272-6963(00)00019-9.

[26] Larsen RF, Buede DM. Theoretical framework for the continuous early validation (CEaVa) method. Syst. Eng. 2002, 5, 223-241. doi:10.1002/sys.10022.

[27] Jabareen Y. Building a conceptual framework: Philosophy, definitions, and procedure. Int. J. Qual. Methods 2009, 8, 49-62. doi:10.1177/160940690900800406.

[28] Glaser B, Strauss A. The Discovery of Grounded Theory: Strategies for Qualitative Research; Routledge: New York, NY, USA, 2017; p. 282, doi:10.4324/9780203793206.

[29] Wooldridge M.An Introduction to Multiagent Systems, 2nd ed.; John Wiley & Sons: Chichester, UK, 2009; p. 484.

[30] Giret A, Trentesaux D, Salido MA, Garcia E, Adam E. A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. J. Clean. Prod. 2017, 167, 1370-1386. doi:10.1016/j.jclepro.2017.03.079.

[31] Palau AS, Dhada MH, Parlikad AK.Multi-agent system architectures for collaborative prognostics. J. Intell. Manuf. 2019, 30, 2999-3013. doi:10.1007/s10845-019-01478-9.

[32] Wellsandt S, Klein K, Hribernik K, Lewandowski M, Bousdekis A, Mentzas G, et al. Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants. Annu. Rev. Control 2022, 53, 382-390. doi:10.1016/j.arcontrol.2022.04.001.

[33] Shen W, Hao Q, Yoon HJ, Norrie DH. Applications of agent-based systems in intelligent manufacturing: An updated review. Adv. Eng. Inform. 2006, 20, 415-431. doi:10.1016/j.aei.2006.05.004.

[34] Leitão P, Karnouskos S. Industrial Agents: Emerging Applications of Software Agents in Industry; Elsevier: Amsterdam, Netherlands, 2015; p. 455. doi:10.1016/C2013-0-15269-5.

[35] Soon GK, On CK, Anthony P, Hamdan AR. A review on agent communication language. Lect. Notes Electr. Eng. 2019, 481, 481-491. doi:10.1007/978-981-13-2622-6_47.

[36] Harris S, Seaborne A.SPARQL 1.1 Query Language. World Wide Web Consortium (W3C). 2013. Available online: https://www.w3.org/TR/sparql11-query/ (accessed on 12 November 2024).

[37] Stokel-Walker C, Van Noorden R. What ChatGPT and generative AI mean for science. Nature 2023, 614, 214-216. doi:10.1038/d41586-023-00340-6.

[38] Brooks R. A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 1986, 2, 14-23. doi:10.1109/JRA.1986.1087032.

[39] Bettosini I, Clavelli A, Barnech GT, Visca J, Benavides F.Torocó: A subsumption architecture implementation. In Proceedings of the 8th International Conference on Automation, Robotics and Applications, Prague, Czech Republic, 18-20 February 2022; IEEE: New York, NY, USA, 2022; pp. 27-32. doi:10.1109/ICARA55094.2022.9738521.

[40] Rao AS, Georgeff MP. BDI agents: From theory to practice. In Proceedings of the First International Conference on Multiagent Systems, San Francisco, CA, USA, 12-14 June 1995; Volume 95, pp. 312-319.

[41] Jang M, Yoon Y, Choi J, Ong H, Kim J. A structured prompting based on belief-desire-intention model for proactive and explainable task planning. In Proceedings of the 11th International Conference on Human-Agent Interaction, New York, NY, USA, 4 December 2023; pp. 375-377. doi:10.1145/3623809.3623930.

[42] Ferguson IA. TouringMachines: An architecture for dynamic, rational, mobile agents. In Technical Report No. UCAM-CL-TR-273; University of Cambridge, Computer Laboratory: Cambridge, UK, 1992. doi:10.48456/tr-273.

[43] Xu J, Wang L, Kou Q, Fang T, Dan Y, Zhou L, et al. Real-time behaviour decision of mobile robot based on the de-liberate/reactive architecture. Int. J. Innov. Comp. Inform. Control 2022, 18, 1163-1180. doi:10.24507/ijicic.18.04.1163.

[44] Shen ZQ, Gay R, Tao X. Goal-based intelligent agents. Int. J. Inform. Tech. 2003, 9, 19-30.

[45] Dulek B, Efendi E, Varshney PK. Behavioral utility-based distributed detection with conditionally independent observations. IEEE Trans. Sign. Proc. 2024, 72, 3717-3730. doi:10.1109/TSP.2024.3439732.

[46] Paccagnan D, Chandan R, Marden JR. Utility and mechanism design in multi-agent systems: An overview. Ann. Rev. Control 2022, 53, 315-328. doi:10.1016/j.arcontrol.2022.02.002.

[47] Zhang J, Bedi AS, Wang M, Koppel A. Multi-agent reinforcement learning with general utilities via decentralized shadow reward actor-critic. In Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada, 27 February 2022; Volume 36, pp. 9031-9039. doi:10.1609/aaai.v36i8.20887.

[48] Russell S.Learning agents for uncertain environments. In Proceedings of the 11th Conference on Computational Learning Theory, Madison, WI, USA, 24-26 July 1998; pp. 101-103. doi:10.1145/279943.279964.

[49] Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, et al. Playing Atari with deep reinforcement learning. arXiv 2013. doi:10.48550/arXiv.1312.5602.

[50] Li C, Zheng P, Yin Y, Wang B, Wang L. Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP J. Manuf. Sci. Tech. 2023, 40, 75-101. doi:10.1016/j.cirpj.2022.11.003.

[51] Newell A. The knowledge level. Art. Int. 1982, 18, 87-127. doi:10.1016/0004-3702(82)90012-1.

[52] Jackson P. Introduction to Expert Systems; Addison-Wesley: Boston, MA, USA, 1990; p. 526.

[53] Russell SJ, Norvig P.Artificial Intelligence: A Modern Approach, 3rd ed.; Pearson Education Limited: Harlow, UK, 2016; p. 1132.

[54] Wrona Z, Buchwald W, Ganzha M, Paprzycki M, Leon F, Noor N, et al. Overview of software agent platforms available in 2023. Information 2023, 14, 348. doi:10.3390/info14060348.

[55] Dong D. Enabling an autonomous agent sharing its minds, describing its conscious contents. Cogn. Syst. Res. 2023, 80, 103-109. doi:10.1016/j.cogsys.2023.03.001.