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

2025-01-02

AI Summary

Senior engineering students struggle to integrate theoretical knowledge to address complex, multidimensional challenges spanning technical, ethical, social, and environmental dimensions in capstone design projects. Method: This study proposes a novel interdisciplinary collaborative framework leveraging multi-agent large language models (LLMs). It innovatively integrates multi-agent systems (MAS) principles with role-based prompt engineering to instantiate specialized LLM agents—each embodying distinct expert personas (e.g., problem modeling, ethical assessment, project management)—and incorporates a Swarm AI–inspired negotiation mechanism to enable dynamic consensus formation and cross-disciplinary knowledge alignment under conflicting objectives. Contribution/Results: Evaluated on six authentic undergraduate capstone design proposals, the framework significantly improves solution completeness, coverage of ethical considerations, and multi-objective协同 optimization capability. It establishes a scalable, AI-augmented pedagogical paradigm for engineering education.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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...

Problem

• Multi-Agent Language Models
• Advanced Design Projects
• Interdisciplinary Problem Solving

Innovation

• Multi-Agent Systems
• Large Language Model Framework
• Interdisciplinary Engineering Education