Automation of Creative Design Representation: A First Step Towards a Co-Pilot for Generative Design

Within the context of AEC, Generative Design enables architects and engineers to solve complex multi-objective modeling problems where a good design solution is non-trivial and requires considering a large set of possible design options in relation to constraints and objectives. While these benefits have been widely demonstrated through past case studies and real-world projects, Generative Design as a design methodology remains a specialized practice accessible only to highly skilled design practitioners with multidisciplinary expertise in architecture and computer programming. One challenging aspect of Generative Design is setting up a creative design representations for spatial problems. This is both technically and creatively complex for designers because it involves 1) developing an algorithmic and rule-based system for the parametric model 2) incorporating and selecting the appropriate requirements 3) ensuring the design representation can describe a diverse set of realistic and constraint-compliant design options and, finally, 4) ensuring the design representation can describe a large, searchable and optimizable solution space to promote solution performance and novelty. To address these requirements we introduce an iterative multi-agent workflow that automatically generates creative design representations from project brief requirements and constraints. We explore the workflow on a curated dataset of spatial layout problems and demonstrate that it outperforms general-purpose coding agents in developing valid, realistic, and diverse design representations. Our work makes parametric modeling fast and affordable for designers, enabling greater use of Generative Design and lowering its technical barrier of entry.