Computation For Architecture: Developing Cognition Beyond Blind Artificial Intelligence

The research presented in this article is part of the 'Computation for Architecture in Python' Project at LAMO-PROURB, FAU-UFRJ. This article describes the development of a tailored methodology for an advanced Python II course, building on an introductory course on the fundamentals of visual and textual programming applied to design. The course, being project-oriented, focuses on adapting computational processes -specifically generative systems - to design. The research explores computational techniques such as Cellular Automata, L-systems, Genetic Algorithms, and Shape Grammars. While these techniques are not new, they often rely on third-party plugins. The authors develop alternatives using self-written programming, employing 'string grammars' with conscious computational processes to create systems through combinatorial optimization with discrete and qualified components. The results highlight advancements in design cognition, elucidating methods, inputs, and outputs, an alternative to common artificial intelligence practices that often rely on superficial statistical methods.