AutoTRIZ: Artificial Ideation with TRIZ and Large Language Models
- URL: http://arxiv.org/abs/2403.13002v3
- Date: Thu, 23 May 2024 02:19:12 GMT
- Title: AutoTRIZ: Artificial Ideation with TRIZ and Large Language Models
- Authors: Shuo Jiang, Jianxi Luo,
- Abstract summary: Theory of Inventive Problem Solving is widely applied for systematic innovation.
The complexity of TRIZ resources and concepts, coupled with its reliance on users' knowledge, experience, and reasoning capabilities, limits its practicality.
This paper proposes AutoTRIZ, an artificial ideation tool that uses LLMs to automate and enhance the TRIZ methodology.
- Score: 2.7624021966289605
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Researchers and innovators have made enormous efforts in developing ideation methods, such as morphological analysis and design-by-analogy, to aid engineering design ideation for problem solving and innovation. Among these, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the most well-known approaches, widely applied for systematic innovation. However, the complexity of TRIZ resources and concepts, coupled with its reliance on users' knowledge, experience, and reasoning capabilities, limits its practicality. Therefore, we explore the recent advances of large language models (LLMs) for a generative approach to bridge this gap. This paper proposes AutoTRIZ, an artificial ideation tool that uses LLMs to automate and enhance the TRIZ methodology. By leveraging the broad knowledge and advanced reasoning capabilities of LLMs, AutoTRIZ offers a novel approach for design automation and interpretable ideation with artificial intelligence. AutoTRIZ takes a problem statement from the user as its initial input, and automatically generates a solution report after the reasoning process. We demonstrate and evaluate the effectiveness of AutoTRIZ through consistency experiments in contradiction detection, and a case study comparing solutions generated by AutoTRIZ with the experts' analyses from the textbook. Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, including SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of artificial ideation for design innovation.
Related papers
- Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [54.247747237176625]
Article explores the convergence of connectionist and symbolic artificial intelligence (AI)
Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic.
Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
arXiv Detail & Related papers (2024-07-11T14:00:53Z) - Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations [0.0]
This paper focuses on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts.
A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototyping process.
The framework describes the seamless incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability.
arXiv Detail & Related papers (2024-02-06T16:00:32Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Resiliency Analysis of LLM generated models for Industrial Automation [0.7018015405843725]
This paper proposes a study of the resilience and efficiency of automatically generated industrial automation and control systems using Large Language Models (LLMs)
The study aims to provide insights into the effectiveness and reliability of automatically generated systems in industrial automation and control, and to identify potential areas for improvement in their design and implementation.
arXiv Detail & Related papers (2023-08-23T13:35:36Z) - Navigating the Complexity of Generative AI Adoption in Software
Engineering [6.190511747986327]
The adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated.
Influencing factors at the individual, technological, and societal levels are analyzed.
arXiv Detail & Related papers (2023-07-12T11:05:19Z) - On Robust Numerical Solver for ODE via Self-Attention Mechanism [82.95493796476767]
We explore training efficient and robust AI-enhanced numerical solvers with a small data size by mitigating intrinsic noise disturbances.
We first analyze the ability of the self-attention mechanism to regulate noise in supervised learning and then propose a simple-yet-effective numerical solver, Attr, which introduces an additive self-attention mechanism to the numerical solution of differential equations.
arXiv Detail & Related papers (2023-02-05T01:39:21Z) - Generative Design Ideation: A Natural Language Generation Approach [7.807713821263175]
This paper aims to explore a generative approach for knowledge-based design ideation by applying the latest pre-trained language models in artificial intelligence (AI)
The AI-generated ideas are not only in concise and understandable language but also able to synthesize the target design with external knowledge sources with controllable knowledge distance.
arXiv Detail & Related papers (2022-03-28T08:11:29Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - KAT: A Knowledge Augmented Transformer for Vision-and-Language [56.716531169609915]
We propose a novel model - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result on the open-domain multimodal task of OK-VQA.
Our approach integrates implicit and explicit knowledge in an end to end encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation.
An additional benefit of explicit knowledge integration is seen in improved interpretability of model predictions in our analysis.
arXiv Detail & Related papers (2021-12-16T04:37:10Z) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.