Expertise, playfulness, analogical reasoning: Three learning mechanisms to train Artificial Intelligence for design applications

Following the success of AI in statistical regression, image generation, and decision-making tasks, new computational tools based on AI have been proposed for design applications since 2014. Engineers have used AI models to improve the efficiency of software for structural analysis and optimisation, whereas architects have started exploring the potential of AI tools for image generation to support conceptual design. This thesis aims to demonstrate that AI can support the design process at an even deeper level. In other words, AI models can autonomously learn design strategies and interact with a designer to suggest design options that are unconstrained and unbiased by a formal description of the design problem, which is often required in structural optimisation applications. AI models can also learn to produce technical descriptions of a design object, whereas current applications of AI in architectural design primarily focus on synthesising visual output. To do so, this thesis examines how AI models can be trained in architectural and structural design and how the trained AI models can be integrated with CAD software to support the design process. This thesis takes the view that training AI in design can be considered as training a novice designer. Therefore, in line with early studies in AI in design conducted in the 1990s, this thesis examines how AI can simulate a designer’s cognition and, in particular, acquire design knowledge by simulating three learning mechanisms relevant to design education: expertise, playfulness, and analogical reasoning. In design education, expertise is related to studying and analysing design precedents; playfulness is linked to model-making, and analogical reasoning pertains to finding inspiration in domains other than architecture, such as nature, art, music, and literature. Through a set of applications, the thesis shows how AI models can be trained in design by simulating the three learning mechanisms and how the trained AI models can be interfaced with CAD software. The applications aim to open a new path for research in AI in design by demonstrating that AI can effectively simulate some aspects of human cognition and interact with a designer through an exchange of visual information. The designer can decide to use the outputs obtained through the interaction with these tools to inform different stages of the design process, which could include problem-framing and decision-making. Although no given tool can be guaranteed to expand a designer’s creativity or automatically lead to outstanding design solutions, the AI models described in this thesis reveal a certain degree of autonomy and thus have a higher potential than other computational techniques to support the design process at a deep level.