ABSTRACT
Generative models are constructs that, given proper input information and a program, can synthesize design alternatives. Earlier, computational approaches were based on techniques from symbolic artificial intelligence and operations research, primarily, to automate the generation of architectural layouts. Recently, a variety of computational techniques from different fields, such as agent-based modeling or deep learning, have been incorporated into the development of new generative models. We are reaching a point where we might not even have to explicitly program a generative model but, instead, have the model learn automatically from data or experience. However, the lack of consistency and rigor in the treatment of underlying ideas and terms is a barrier in the teaching of generative design. To address this, we introduce a taxonomy of the components of generative models, which are organized in three levels according to their function: expression, synthesis, or learning. Existing or novel generative models can be represented in the taxonomy as a combination of multiple building blocks. We introduced this taxonomy in computational design courses at Carnegie Mellon University, and it has resulted in interesting projects——some of which are highlighted in this chapter as examples.
