Large Language Model-Enabled Multi-Agent Manufacturing Systems

Jonghan Lim, Birgit Vogel-Heuser, Ilya Kovalenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Traditional manufacturing faces challenges adapting to dynamic environments and quickly responding to manufacturing changes. The use of multi-agent systems has improved adaptability and coordination but requires further advancements in rapid human instruction comprehension, operational adaptability, and coordination through natural language integration. Large language models like GPT-3.5 and GPT-4 enhance multi-agent manufacturing systems by enabling agents to communicate in natural language and interpret human instructions for decision-making. This research introduces a novel framework where large language models enhance the capabilities of agents in manufacturing, making them more adaptable, and capable of processing context-specific instructions. A case study demonstrates the practical application of this framework, showing how agents can effectively communicate, understand tasks, and execute manufacturing processes, including precise G-code allocation among agents. The findings highlight the importance of continuous large language model integration into multi-agent manufacturing systems and the development of sophisticated agent communication protocols for a more flexible manufacturing system.

Original languageEnglish
Title of host publication2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PublisherIEEE Computer Society
Pages3940-3946
Number of pages7
ISBN (Electronic)9798350358513
DOIs
StatePublished - 2024
Event20th IEEE International Conference on Automation Science and Engineering, CASE 2024 - Bari, Italy
Duration: 28 Aug 20241 Sep 2024

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Country/TerritoryItaly
CityBari
Period28/08/241/09/24

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