用多个AI角色协作生成工业故障分析报告
Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information
- 设计多角色LLM系统，分步协作生成FMEA文档
- 通过动态对话迭代优化内容，提升准确性
- 适合工业设备监测领域，支持模板化流程
本文提出一种名为Chat-of-Thought的新型多智能体系统，用于生成工业资产的失效模式与影响分析（FMEA）文档。该系统采用具有特定职责的多个基于大语言模型（LLM）的智能体，结合先进AI技术与动态任务路由，优化FMEA表格的生成与验证过程。其核心创新在于引入‘思维对话’机制，通过动态、多角色驱动的讨论实现内容的迭代优化。研究聚焦工业设备监控应用，揭示关键挑战，并展示该系统在交互式、模板驱动的工作流与上下文感知的智能体协作中解决这些问题的潜力。
原文摘要 · Abstract (English)
This paper presents a novel multi-agent system called Chat-of-Thought, designed to facilitate the generation of Failure Modes and Effects Analysis (FMEA) documents for industrial assets. Chat-of-Thought employs multiple collaborative Large Language Model (LLM)-based agents with specific roles, leveraging advanced AI techniques and dynamic task routing to optimize the generation and validation of FMEA tables. A key innovation in this system is the introduction of a Chat of Thought, where dynamic, multi-persona-driven discussions enable iterative refinement of content. This research explores the application domain of industrial equipment monitoring, highlights key challenges, and demonstrates the potential of Chat-of-Thought in addressing these challenges through interactive, template-driven workflows and context-aware agent collaboration.
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