标题： 利用多智能体LLM解决复杂工程问题：面向高年级设计项目的框架 显示英文标题

标题： Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects

摘要： 多智能体大型语言模型（LLMs）因其在复杂问题解决、决策和规划任务中利用集体智慧的能力而受到广泛关注。 这与群体智慧的概念相一致，其中不同的代理共同贡献以生成有效的解决方案，使其特别适合教育环境。 高级设计项目，也称为毕业设计或最终项目，是工程教育中的关键部分，因为它们将理论知识与实际应用相结合，培养批判性思维、团队合作和解决现实问题的技能。 在本文中，我们探讨了多智能体LLMs在支持工程学生进行的这些高级设计项目的应用，这些项目通常涉及跨学科的考虑和冲突的目标，例如在优化技术性能的同时解决伦理、社会和环境问题。 我们提出一个框架，其中不同的LLM代理代表不同的专家观点，如问题定义代理、系统复杂性代理、社会和伦理代理或项目经理，从而促进全面的问题解决方法。 该实现利用标准的多智能体系统（MAS）概念，如协调、合作和协商，并结合提示工程为每个代理开发多样化的角色。 这些代理进行丰富的协作对话，以模拟人类工程团队，在群体AI原则的指导下，高效地平衡个体贡献以达成统一的解决方案。 我们将这些技术适应为LLM代理的合作结构，鼓励类似现实世界高级设计项目的跨学科推理和协商。 为了评估该框架的有效性，我们收集了六个工程和计算机科学的提案... 显示英文摘要

摘要： 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...