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.

翻译：本文介绍了一种新型自动化系统，用于生成建筑方案设计，旨在简化多户房地产开发项目初期的复杂决策过程。该方法融合了生成式人工智能（神经推理）与数学规划求解器（符号推理）的互补优势，同时解决了建筑方案设计中对专家见解的依赖及技术挑战。针对整体建筑设计中大规模且相互关联的设计决策需求，我们提出了一种新颖的序列神经符号推理方法，模拟了从初始概念到详细布局的传统建筑设计流程。为消除人工构建成本函数以近似目标函数的需求，我们提出利用神经推理生成符号求解器可处理的约束条件与成本函数的解决方案。同时引入各设计阶段的反馈循环，确保神经推理与符号推理的紧密集成。本方法基于GPT-4开发而无需额外训练，通过实际建筑对比研究验证了其有效性。该方法能够根据对周边环境的理解生成多样化的建筑设计方案，展现了其变革建筑方案设计领域的潜力。