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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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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.

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概括
此摘要是机器生成的。

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.

关键词:
通过LLM指导的代理商.基于代理的建模.大型语言模型.模拟模拟是指一个模拟模拟器.群众情报是一个群众情报.

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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.