Abstract
Previous automated building layout studies focus more on optimization than design diversity. However, designers constantly generate new goals in a design task owing to the complex constraints and ill-defined evaluation criteria, and then they have to repeat the optimization consequently. We consider that a more interactive human-machine cooperative design should rapidly create considerable design alternatives with performance analysis in preliminary design period for architects to select. Inspired by Monte Carlo Tree Search (MCTS), we propose an algorithm which can generate various acceptable solutions rapidly for building layout problem. In this article, this algorithm is applied to a kindergarten design project to investigate its efficacy, and to discuss its potential for universal building layout problems and machine learning.
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Acknowledgment
This research was supported by National Natural Science Foundation of China (51578277) and Major Program of National Natural Science Foundation of China (51538005).
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Cao, S., Zhou, Z., Tong, Z. (2020). Application of Algorithmic Generation to Kindergarten Design. In: Yuan, P., Xie, Y., Yao, J., Yan, C. (eds) Proceedings of the 2019 DigitalFUTURES . CDRF 2019. Springer, Singapore. https://doi.org/10.1007/978-981-13-8153-9_19
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