CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id caadria2020_306
authors Akizuki, Yuta, Bernhard, Mathias, Kakooee, Reza, Kladeftira, Marirena and Dillenburger, Benjamin
year 2020
title Generative Modelling with Design Constraints - Reinforcement Learning for Object Generation
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 445-454
summary Generative design has been explored to produce unprecedented geometries, nevertheless design constraints are, in most cases, second-graded in the computational process. In this paper, reinforcement learning is deployed in order to explore the potential of generative design satisfying design objectives. The aim is to overcome the three issues identified in the state of the art: topological inconsistency, less variations in style and unpredictability in design. The goal of this paper is to develop a machine learning framework, which works as an intellectual design interpreter capable of codifying an input geometry to form a new geometry. Experiments demonstrate that the proposed method can generate a family of tables of unique aesthetics, satisfying topological consistency under given constraints.
keywords generative design; computational design; data-driven design; reinforcement learning; machine learning
series CAADRIA
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