Towards Ai-Assisted Design Workflows For An Expanded Design Space
The scope of this paper is to formulate and evaluate the structure of a viable design workflow that combines a variety of computational tools and uses artificial intelligence (AI) to enhance the designer’s capacity to explore design options within an expanded design space. In light of the autonomous and progressively post-anthropocentric generative capability of recent AI strategies for architectural design, we are interested in investigating some of the challenges involved in the insertion of such AI strategies into a new generative design system, involving data curation and the placement of any AI-assisted model in the overall workflow, as well as its (AI’s) reciprocity with other computational methods such as discrete assembly and agent-based modeling. The paper presents our interrogation of the proposed AI-assisted framework, demonstrated in experiments of formulating multiple design workflows following different strategies. The workflow strategies show that integrating AI networks into a framework with other computational tools affords a different kind of design exploration than other methods; the prospect of novel solutions is heavily dependent on the interconnectedness of such methods and the dataset curation process. Collectively, the work contributes to innovation in architectural education and practice through enhancing scientific research (in line with UN Sustainable Development Goal 9).
Keywords: Artificial Intelligence; Deep Learning; Ai-Assisted Design Workflows; Design Space Exploration; Generative Systems; Sdg 9