用多个专家型AI代理协同解决创新难题，模仿人类发明思维。
TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation
- 设计多个具备专长的AI代理，分工协作完成TRIZ创新流程。
- 在工程案例中生成多样且富有创意的解决方案，验证有效性。
- 适合需要跨领域创新的科研与工程团队参考使用。
TRIZ（发明问题解决理论）是一种结构化、基于知识的创新框架，用于抽象问题并寻找创造性解决方案。然而，其应用常受限于复杂性和跨学科知识要求。大型语言模型（LLM）的发展为自动化部分流程提供了新可能。尽管已有研究探索单个LLM在TRIZ中的应用，本文提出一种多智能体方法：构建一个基于LLM的多代理系统——TRIZ Agents，各代理拥有特定能力与工具访问权限，协同依据TRIZ方法论解决发明问题。该系统通过具备不同领域专长的代理，高效推进TRIZ各步骤。目标是模拟和再现发明过程的语言代理机制。我们基于一个工程案例评估该多代理团队应对复杂创新挑战的有效性，结果表明代理协作能生成多样化、具有创造性的解决方案。本研究推动了人工智能驱动创新的未来发展，展示了在复杂构思任务中去中心化协作的优势。
原文摘要 · Abstract (English)
TRIZ, the Theory of Inventive Problem Solving, is a structured, knowledge-based framework for innovation and abstracting problems to find inventive solutions. However, its application is often limited by the complexity and deep interdisciplinary knowledge required. Advancements in Large Language Models (LLMs) have revealed new possibilities for automating parts of this process. While previous studies have explored single LLMs in TRIZ applications, this paper introduces a multi-agent approach. We propose an LLM-based multi-agent system, called TRIZ agents, each with specialized capabilities and tool access, collaboratively solving inventive problems based on the TRIZ methodology. This multi-agent system leverages agents with various domain expertise to efficiently navigate TRIZ steps. The aim is to model and simulate an inventive process with language agents. We assess the effectiveness of this team of agents in addressing complex innovation challenges based on a selected case study in engineering. We demonstrate the potential of agent collaboration to produce diverse, inventive solutions. This research contributes to the future of AI-driven innovation, showcasing the advantages of decentralized problem-solving in complex ideation tasks.
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