Design Process with Generative AI and Thinking Methods: Divergence of Ideas Using the Fishbone Diagram Method

Yuhi Maeda, Jun'ichi Ito, Keita Kado
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Abstract

In 2022, high-performance generative AI — such as Stable Diffusion and ChatGPT — were reported upon and released amid increasing momentum for the utilization of such generative AI. In the field of architecture, generative AI is expected to not only be used for task automation, but as a means of diverging ideas as well, especially in the planning stage of architectural design. However, effective application methods have not yet been reported.Therefore, this study proposes a concept-making method that combines generative AI and ways to diverge ideas in architectural design and proposes a tuning method for ChatGPT to enable more effective dialogue. In the proposed method, ChatGPT is involved as a member in group work settings that aims to create concepts and initial designs using the fishbone diagram, one of the ways to list and categorize factors and ideas to achieve goals. In addition, ChatGPT is tuned to obtain more effective factors and ideas, particularly those related to spatial composition and shapes by inputting text regarding architectural design and specific architects.The proposed method was tested via case studies that created concepts and initial designs for an actual architectural competition. The results show that external ideas obtained from generative AI inspire the fishbone diagram process. The concepts and designs created seem imaginative and appropriate for competition.
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生成式人工智能的设计过程与思维方法:鱼骨图方法的思想分歧
2022年,高性能生成式人工智能(如Stable Diffusion和ChatGPT)被报道并发布,这种生成式人工智能的使用势头日益强劲。在建筑领域,生成式人工智能不仅可以用于任务自动化,还可以作为发散思想的手段,特别是在建筑设计的规划阶段。然而,有效的应用方法尚未见报道。因此,本研究提出了一种将生成式人工智能与建筑设计中的思想分歧相结合的概念生成方法,并提出了一种ChatGPT的调优方法,以实现更有效的对话。在提出的方法中,ChatGPT作为小组工作设置的成员参与其中,旨在使用鱼骨图创建概念和初始设计,鱼骨图是列出和分类实现目标的因素和想法的方法之一。此外,ChatGPT通过输入有关建筑设计和特定建筑师的文本来获得更有效的因素和想法,特别是与空间构成和形状相关的因素和想法。提出的方法通过案例研究进行了测试,这些案例研究为实际的建筑竞赛创造了概念和初步设计。结果表明,生成式人工智能获得的外部思想启发了鱼骨图过程。所创造的概念和设计似乎富有想象力,适合竞争。
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