Design with shape grammars and reinforcement learning


Shape grammars are a powerful and appealing formalism for automatic shape generation in computer-based design systems. This paper presents a proposal complementing the generative power of shape grammars with reinforcement learning techniques. We use simple (naive) shape grammars capable of generating a large variety of different designs. In order to generate those designs that comply with given design requirements, the grammar is subject to a process of machine learning using reinforcement learning techniques. Based on this method, we have developed a system for architectural design, aimed at generating two-dimensional layout schemes of single-family housing units. Using relatively simple grammar rules, we learn to generate schemes that satisfy a set of requirements stated in a design guideline. Obtained results are presented and discussed.

DOI: 10.1016/j.aei.2012.12.004

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@article{RuizMontiel2013DesignWS, title={Design with shape grammars and reinforcement learning}, author={Manuela Ruiz-Montiel and Javier Boned and Juan Gavilanes and Eduardo Jim{\'e}nez and Lawrence Mandow and Jos{\'e}-Luis P{\'e}rez-de-la-Cruz}, journal={Advanced Engineering Informatics}, year={2013}, volume={27}, pages={230-245} }