由大型语言模型驱动的多代理系统:在群集智能中的应用
Cristian Jimenez-Romero1, Alper Yegenoglu2, Christian Blum3
1ETIS Laboratory, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France.
Frontiers in artificial intelligence
|June 5, 2025
概括
大型语言模型 (LLM) 现在驱动多代理模拟,用适应性提示取代编码行为. 这一创新增强了对复杂系统的研究,如食和鸟群的研究.
科学领域:
- 人工智能的人工智能
- 计算科学 计算科学
- 复杂的系统复杂的系统.
背景情况:
- 传统的多代理模拟依赖于硬编码的代理行为.
- 在复杂系统中研究新出现的行为往往需要灵活的代理程序编程.
- 群体智能模型自然自我组织,但可以被固定的规则限制.
研究的目的:
- 将大型语言模型 (LLM) 集成到多代理模拟中.
- 用LLM驱动的提示来取代硬编码的代理程序,以适应性行为.
- 探索LLM在建模复杂系统和群集智能的潜力.
主要方法:
- 开发了一个工具链,通过Python将LLMs与NetLogo模拟平台集成在一起.
- 通过OpenAI API利用GPT-4o进行提示驱动的代理行为生成.
- 实施了结构化,基于规则的和自主性的,基于知识的提示.
- 将工具链应用于殖民地食和鸟群模拟.
主要成果:
- 已证明,LLM驱动的代理可以适应性地响应环境数据.
- 展示了使用LLM提示程序研究自我组织过程的能力.
- 在多代理模拟中成功诱导新兴行为.
- 验证了模拟群集智能现象的方法.
结论:
- 法律学集成为多代理模拟提供了一个新的范式.
- 提示驱动的行为使智能系统的模型更具动态性和现实性.
- 这种方法有助于探索以大自然为灵感的新兴现象.
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