It is shown how Augmented Transition Networks (ATN) can be gradually programmed with shape grammar structures. This work is inspired by natural language parsing. Another major reference is the space-between or spatium assumption. An application is given with a simulation of Palladio villas. Then is shown that ATN frames can be encoded in a way that allows their use without specific knowledge of computer modelling. Connections between human and machine learning are touched on.