城市流动中的认知代理:将LLM推理集成到多代理模拟中
Christian Calderón1, Pasqual Martí1, Jaume Jordán1
1Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
本研究介绍了城市移动模拟中的认知代理的大型语言模型 (LLM),增强现实性和适应交通中断. 使用LLM驱动的代理可以提高模拟的灵活性和可解释性.
科学领域:
- 城市规划和交通科学 城市规划和交通科学
- 人工智能和基于代理的建模.
- 认知科学和行为经济学.
背景情况:
- 城市移动系统面临着可持续性,公平性和弹性方面的挑战.
- 传统的基于代理的模型 (ABM) 缺乏认知深度来模拟适应性用户行为.
- 环境压力加剧了城市移动系统的复杂性.
研究的目的:
- 通过使用大型语言模型 (LLM) 提出一种新的认知代理架构.
- 提高城市移动模拟的现实性,灵活性和可解释性.
- 在应对运输中断时建模适应性用户行为.
主要方法:
- 开发了一个基于LLM的认知代理架构,具有记忆驱动的规划,反思和适应.
- 将LLM代理集成到基于代理的SimFleet模拟器中.
- 对320多人进行了为期20天的模拟,并进行了废除研究.
主要成果:
- 在稳定和中断的运输条件下观察到代理物的新兴适应模式.
- 量化了短期和长期记忆模块对代理推理的影响.
- 经过LLM驱动的代理商有能力动态生成,调整和反思旅行计划.
结论:
- 由LLM驱动的认知代理显著提高了城市移动模拟的现实性和灵活性.
- 拟议的架构为用户对中断的响应提供了更好的解释性.
- 这种方法推进了复杂的城市系统的基于代理的建模.
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