This paper presents a novel design of a multi-agent system framework that applies a large language model (LLM) to automate the parametrization of process simulations in digital twins. We propose a multi-agent framework that includes four types of agents: observation, reasoning, decision and summarization. By enabling dynamic interaction between LLM agents and simulation model, the developed system can automatically explore the parametrization of the simulation and use heuristic reasoning to determine a set of parameters to control the simulation to achieve an objective. The proposed approach enhances the simulation model by infusing it with 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 are available at a GitHub Repository: https://github.com/YuchenXia/LLMDrivenSimulation

翻译：本文提出了一种新颖的多智能体系统框架设计，该框架应用大语言模型（LLM）实现数字孪生中过程仿真参数化的自动化。我们提出了一个包含四种智能体类型（观察、推理、决策与总结）的多智能体框架。通过实现LLM智能体与仿真模型之间的动态交互，所开发的系统能够自动探索仿真的参数化配置，并利用启发式推理确定一组参数来控制仿真以实现特定目标。该方法通过向仿真模型注入来自LLM的启发式知识来增强模型能力，并支持自主搜索可行的参数化方案以解决用户任务。此外，该系统有望通过辅助复杂决策过程来提高用户友好性并减轻人类用户的认知负荷。通过案例研究验证了系统的有效性和功能性，可视化演示可在GitHub仓库获取：https://github.com/YuchenXia/LLMDrivenSimulation