TY - GEN
T1 - Large Language Model-Enabled Multi-Agent Manufacturing Systems
AU - Lim, Jonghan
AU - Vogel-Heuser, Birgit
AU - Kovalenko, Ilya
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85208226591&partnerID=8YFLogxK
U2 - 10.1109/CASE59546.2024.10711432
DO - 10.1109/CASE59546.2024.10711432
M3 - Conference contribution
AN - SCOPUS:85208226591
T3 - IEEE International Conference on Automation Science and Engineering
SP - 3940
EP - 3946
BT - 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Y2 - 28 August 2024 through 1 September 2024
ER -