Zero-shot Sequential Neuro-symbolic Reasoning for Automatically
  Generating Architecture Schematic Designs
Milin Kodnongbua Lawrence H. Curtis Adriana Schulz
This paper introduces a novel automated system for generating architecture
schematic designs aimed at streamlining complex decision-making at the
multifamily real estate development project's outset. Leveraging the combined
strengths of generative AI (neuro reasoning) and mathematical program solvers
(symbolic reasoning), the method addresses both the reliance on expert insights
and technical challenges in architectural schematic design. To address the
large-scale and interconnected nature of design decisions needed for designing
a whole building, we proposed a novel sequential neuro-symbolic reasoning
approach, emulating traditional architecture design processes from initial
concept to detailed layout. To remove the need to hand-craft a cost function to
approximate the desired objectives, we propose a solution that uses neuro
reasoning to generate constraints and cost functions that the symbolic solvers
can use to solve. We also incorporate feedback loops for each design stage to
ensure a tight integration between neuro and symbolic reasoning. Developed
using GPT-4 without further training, our method's effectiveness is validated
through comparative studies with real-world buildings. Our method can generate
various building designs in accordance with the understanding of the
neighborhood, showcasing its potential to transform the realm of architectural
schematic design.