| id |
caadria2024_148 |
| authors |
Kimm, Geoff, White, Marcus and Burry, Mark |
| year |
2024 |
| title |
Adapting the Software Design Pattern Model for AI-Enabled Design Computing |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 49–58 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.049
|
| summary |
Exponential AI development requires an adaptation to new technology by traditionally reluctant architects and allied practitioners. This paper examines the potential of the software design pattern (SDP) model, used in software engineering to capture and reapply designs, as one underpinning. Patterns have creativity and pedagogical benefits in parametric modelling, yet consideration of AI and broader design computing as well as the derivation and versatility implied by an SDP model are underexamined. This research questions how, in an AI context, new patterns may evolve for varied AI levels and non-geometrical features. It is undertaken in the Unity game engine with critical application of two prominent extant patterns as a computational workflow design response to a real-world citizen engagement scenario. A novel, feature-agnostic pattern is derived with a simple AI model and is verified for other AI models. The work concludes design computing patterns can abstract existing pattern knowledge to flexibly evolve and apply across rapidly changing AI-enabled design computing contexts and thereby assist practitioners to positively respond to AI advances. |
| keywords |
artificial intelligence, computational design, software design patterns, architectural practice, Unity 3D, intelligent agents |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (711,283 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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