基于LLM的多代理系统通过代理的动态集成实现自动扩展
Ravindu Perera1,2, Anuradha Basnayake1, Manjusri Wickramasinghe1
1University of Colombo School of Computing, University of Colombo, Colombo, Sri Lanka.
Frontiers in artificial intelligence
|September 29, 2025
概括
基于动态大型语言模型的多代理系统 (基于LLM的MAS) 通过实时自动生成代理来提高适应性和任务性能. 这克服了复杂挑战的静态架构的局限性.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
背景情况:
- 基于大型语言模型的多代理系统 (基于LLM的MAS) 提供了与个人代理相比的协作优势.
- 目前基于LLM的MAS架构是刚性的,在动态环境中缺乏适应性和可扩展性.
研究的目的:
- 在基于LLM的MAS中引入用于自动生成代理的新方法.
- 为了提高基于LLM的MAS的灵活性和性能,以应对不断变化的环境.
主要方法:
- 拟议的初始自动代理生成 (IAAG) 和动态实时代理生成 (DRTAG) 方法.
- 利用先进的提示工程技术 (人格模式,链条,几次拍摄提示) 来创建代理.
- 针对LLM生成文本的评分进行了调整的评价指标.
主要成果:
- 与静态MAS相比,DRTAG显著提高了系统适应性和任务性能.
- IAAG增强了最初的系统灵活性和情境相关的代理人创建.
- 自动生成剂减少了对人类干预的需求.
结论:
- 基于动态LLM的MAS有效地解决了静态架构的限制.
- 这些进步为复杂的现实世界问题提供了更强大,更可扩展的解决方案.
- 开辟了各种领域的创新应用的道路.
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