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
- AIDE: AI-Driven Exploration in the Space of Code [6.401493599308353]
We introduce AI-Driven Exploration (AIDE), a machine learning engineering agent powered by large language models (LLMs)
AIDE frames machine learning engineering as a code optimization problem, and formulates trial-and-error as a tree search in the space of potential solutions.
By strategically reusing and refining promising solutions, AIDE effectively trades computational resources for enhanced performance.
arXiv Detail & Related papers (2025-02-18T18:57:21Z) - An Interpretable Automated Mechanism Design Framework with Large Language Models [26.89126917895188]
Mechanism has long been a cornerstone of economic theory, with traditional approaches relying on mathematical derivations.
Recent automated approaches, including differentiable economics with neural networks, have emerged for designing payments and allocations.
We introduce a novel framework that reformulates mechanism design as a code generation task.
arXiv Detail & Related papers (2025-02-16T12:33:03Z) - Toward Neurosymbolic Program Comprehension [46.874490406174644]
We advocate for a Neurosymbolic research direction that combines the strengths of existing DL techniques with traditional symbolic methods.
We present preliminary results for our envisioned approach, aimed at establishing the first Neurosymbolic Program framework.
arXiv Detail & Related papers (2025-02-03T20:38:58Z) - A Novel Idea Generation Tool using a Structured Conversational AI (CAI) System [0.0]
This paper presents a novel conversational AI-enabled active ideation interface as a creative idea-generation tool to assist novice designers.
It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI)
Integrating such AI models with ideation creates what we refer to as an Active Ideation scenario, which helps foster continuous dialogue-based interaction, context-sensitive conversation, and prolific idea generation.
arXiv Detail & Related papers (2024-09-09T16:02:27Z) - Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making.
Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations.
Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
arXiv Detail & Related papers (2024-08-17T11:49:53Z) - 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) - Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review [1.6006550105523192]
Review explores the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs)
Examines both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge.
Review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.
arXiv Detail & Related papers (2023-10-23T09:15:18Z) - 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.