用多智能体大模型辅助工科毕设，模拟跨学科团队协作解决问题。
Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects
- 构建不同专家角色的AI智能体，协同完成复杂工程问题。
- 在6个真实毕设提案上验证，显著提升方案综合质量。
- 适合教育场景，培养学生跨学科思维与团队协作能力。
多智能体大型语言模型（Multi-Agent LLMs）因其在复杂问题求解、决策与规划中的集体智能潜力而受到广泛关注，其理念类似于‘群体智慧’——多样化的智能体共同贡献，生成高效解决方案，特别适用于教育场景。本科毕业设计项目（即顶点或最后一年项目）是工程教育的关键环节，能够将理论知识与实际应用结合，培养批判性思维、团队合作及解决现实问题的能力。本文探讨了多智能体大模型在支持工程类学生毕业设计中的应用，这些项目通常涉及多学科考量和相互冲突的目标，如在优化技术性能的同时兼顾伦理、社会与环境因素。我们提出一个框架，其中不同的LLM智能体代表各类专家视角，如问题定义、系统复杂性、社会伦理或项目经理等，从而实现全面的问题求解。该实现基于标准多智能体系统（MAS）概念，包括协调、合作与协商，并通过提示工程为各智能体设定多样化身份。这些智能体进行丰富的协作对话，模拟真实工程团队的工作方式，遵循群体智能（swarm AI）原则，有效平衡个体贡献以达成统一方案。我们针对该框架设计了协作结构，促进跨学科推理与谈判，类似真实的毕业设计项目。为评估其有效性，我们在6个工程与计算机科学的毕业设计提案上进行了测试。
原文摘要 · Abstract (English)
Multi-Agent Large Language Models (LLMs) are gaining significant attention for their ability to harness collective intelligence in complex problem-solving, decision-making, and planning tasks. This aligns with the concept of the wisdom of crowds, where diverse agents contribute collectively to generating effective solutions, making it particularly suitable for educational settings. Senior design projects, also known as capstone or final year projects, are pivotal in engineering education as they integrate theoretical knowledge with practical application, fostering critical thinking, teamwork, and real-world problem-solving skills. In this paper, we explore the use of Multi-Agent LLMs in supporting these senior design projects undertaken by engineering students, which often involve multidisciplinary considerations and conflicting objectives, such as optimizing technical performance while addressing ethical, social, and environmental concerns. We propose a framework where distinct LLM agents represent different expert perspectives, such as problem formulation agents, system complexity agents, societal and ethical agents, or project managers, thus facilitating a holistic problem-solving approach. This implementation leverages standard multi-agent system (MAS) concepts such as coordination, cooperation, and negotiation, incorporating prompt engineering to develop diverse personas for each agent. These agents engage in rich, collaborative dialogues to simulate human engineering teams, guided by principles from swarm AI to efficiently balance individual contributions towards a unified solution. We adapt these techniques to create a collaboration structure for LLM agents, encouraging interdisciplinary reasoning and negotiation similar to real-world senior design projects. To assess the efficacy of this framework, we collected six proposals of engineering and computer science of...
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