Space Allocation Techniques (SAT)

Architects and urban designers use space allocation to develop layouts constrained by project-specific attributes of spaces and relations between them. The space allocation problem (SAP) is a general class of computable problems that eluded automation due to combinatorial complexity and diversity of architectural forms. In this paper, we propose a solution to the space allocation problem using reinforcement learning (RL). In RL, an artificial agent interacts with a simulation of the design problem to learn the optimal spatial organization of a layout using a feedback mechanism based on project-specific constraints. Compared to supervised learning, where the scope of the design problem is restricted by the availability of prior samples, we developed a general approach using RL to address novel design problems, represented as SAP. We integrated the proposed solution to SAP with numerous geometry modules, collectively defined as the space allocation techniques (SAT). In this implementation, the optimization and generative modules are decoupled such that designers can connect the modules in various ways to generate layouts with desired geometric and topological attributes. The outcome of this research is a user-friendly, freely accessible Rhino Grasshopper (C#) plugin, namely, the Design Optimization Toolset or DOTs, a compilation of the proposed SAT. DOTs allows designers to interactively develop design alternatives that reconcile project-specific constraints with the geometric complexity of architectural forms. We describe how professional designers have applied DOTs in space planning, site parcellation, massing, and urban design problems that integrate with performance analysis to enable a holistic, semi-automated design exploration.