Large Language Model-Enabled Multi-Agent Manufacturing Systems

TL;DR

This paper presents a framework integrating large language models into multi-agent manufacturing systems to improve adaptability, natural language communication, and human instruction understanding, demonstrated through a practical case study.

Contribution

It introduces a novel framework that leverages large language models to enhance agent communication and decision-making in manufacturing environments.

Findings

Agents can understand and execute human instructions effectively.

Improved communication protocols enable better coordination among agents.

The framework demonstrates practical applicability in manufacturing tasks.

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.