TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation

TL;DR

This paper presents TRIZ agents, a multi-agent LLM system designed to collaboratively solve inventive problems using the TRIZ methodology, aiming to automate and enhance innovation processes.

Contribution

It introduces a novel multi-agent LLM framework for TRIZ-based innovation, enabling specialized agents to collaboratively address complex inventive challenges.

Findings

Agents successfully generated diverse inventive solutions

Multi-agent collaboration improved problem-solving efficiency

Case study demonstrated potential for AI-driven innovation

Abstract

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.