LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins

• URL: http://arxiv.org/abs/2405.18092v2
• Date: Mon, 22 Jul 2024 14:03:48 GMT
• Title: LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins
• Authors: Yuchen Xia, Daniel Dittler, Nasser Jazdi, Haonan Chen, Michael Weyrich,
• Abstract summary: This paper presents a novel framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. The proposed approach enhances the usability of simulation model by infusing it with knowledges from LLM. The system has the potential to increase user-friendliness and reduce the cognitive load on human users.
• Score: 4.773175285216063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing, reasoning, decision-making, and summarizing, enabling them to dynamically interact with digital twin simulations to explore parametrization possibilities and determine feasible parameter settings to achieve an objective. The proposed approach enhances the usability of simulation model by infusing it with knowledge heuristics from LLM and enables autonomous search for feasible parametrization to solve a user task. Furthermore, the system has the potential to increase user-friendliness and reduce the cognitive load on human users by assisting in complex decision-making processes. The effectiveness and functionality of the system are demonstrated through a case study, and the visualized demos and codes are available at a GitHub Repository: https://github.com/YuchenXia/LLMDrivenSimulation

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