Augmented Computational Design: Methodical Application of Artificial Intelligence in Generative Design

TL;DR

This paper explores how artificial intelligence can enhance generative design processes in architecture by improving decision-making and navigating complex design spaces through performance-based approaches.

Contribution

It provides a methodological framework for integrating AI into generative design to better handle complex decision spaces and optimize architectural outcomes.

Findings

AI augments decision-making in generative design.

Performance indicators guide AI-driven design choices.

AI facilitates navigation of complex design decision spaces.

Abstract

This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in Artificial Intelligence for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.