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

Kamil Szczepanik, Jarosław A. Chudziak

2025

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.

Paper Citation

in Harvard Style

Szczepanik K. and Chudziak J. (2025). TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 196-207. DOI: 10.5220/0013321900003890

in Bibtex Style

@conference{icaart25,
author={Kamil Szczepanik and Jarosław Chudziak},
title={TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={196-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013321900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation
SN - 978-989-758-737-5
AU - Szczepanik K.
AU - Chudziak J.
PY - 2025
SP - 196
EP - 207
DO - 10.5220/0013321900003890
PB - SciTePress