让普通人用对话操控复杂仿真系统
Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making
- 用对话接口连接大模型与仿真系统
- 大模型获得真实世界结构化知识
- 适合需要决策支持的非技术用户
仿真系统虽能精确复现现实系统，但因复杂性难以被非技术人员使用。大语言模型（LLMs）提供自然语言交互，却缺乏对复杂现实动态的因果理解。本文提出仿真代理框架，融合仿真模型与大模型优势：利用大模型的对话能力让用户无缝操作复杂仿真系统，同时通过仿真提供准确、结构化的现实表征，为模型提供可靠依据。该方法为实证验证提供了稳健且通用的基础，在多个领域具有广泛适用性。
原文摘要 · Abstract (English)
Simulations, although powerful in accurately replicating real-world systems, often remain inaccessible to non-technical users due to their complexity. Conversely, large language models (LLMs) provide intuitive, language-based interactions but can lack the structured, causal understanding required to reliably model complex real-world dynamics. We introduce our simulation agent framework, a novel approach that integrates the strengths of both simulation models and LLMs. This framework helps empower users by leveraging the conversational capabilities of LLMs to interact seamlessly with sophisticated simulation systems, while simultaneously utilizing the simulations to ground the LLMs in accurate and structured representations of real-world phenomena. This integrated approach helps provide a robust and generalizable foundation for empirical validation and offers broad applicability across diverse domains.
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