Extract

This position paper describes a pathway and methodology towards creative systems in architectural design. Drawing from creativity research and strategic design methods, an agile approach to exploration of deep learning technology in the context of architectural optimisation was developed. The investigation proposed and defined the nature of a framework, which explored ways of integrating architectural shape design with machine intelligence. Furthermore, the paper elaborates the implications and potential for impact of deep learning techniques on advancing human-computer- interaction for architectural optimisation. However, the described framework might be used as a design scheme for an active tool to drive design processes and support decision-making in early stages of architectural design. The components of the framework defined interfaces and critical points of investigation for application of the presented methodology in creative practice. In this way the research contributes to the theoretical and methodological development of creative systems research. At its heart, this generative design study involved the definition of a clear research trajectory, challenges and opportunities of supporting creative practice by means of design systems. Finally, the potential of machine intelligence to generate creative work with and without human guidance or performance criteria was examined.

Keywords: Strategic Design, Deep Learning, Architectural Optimisation, Generative Design

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