Large Language Model-Enabled Multi-Agent Manufacturing Systems
- URL: http://arxiv.org/abs/2406.01893v2
- Date: Fri, 21 Jun 2024 14:54:46 GMT
- Title: Large Language Model-Enabled Multi-Agent Manufacturing Systems
- Authors: Jonghan Lim, Birgit Vogel-Heuser, Ilya Kovalenko,
- Abstract summary: This research introduces a novel framework where large language models enhance the capabilities of agents in manufacturing.
A case study demonstrates the practical application of this framework, showing how agents can effectively communicate, understand tasks, and execute manufacturing processes.
- Score: 4.139369134071008
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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