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id caadria2019_462
authors Koh, Immanuel, Amorim, Pedro and Huang, Jeffrey
year 2019
title Machinic Design Inference: from Pokémon to Architecture - A Probabilistic Machine Learning Model for Generative Design using Game Levels Abstractions
source M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 421-430
summary In this paper, we use a probabilistic machine learning model, trained with a corpus of existing game levels tile-maps, to study the potential of an inference design system for architectural design. Our system is able to extract implicit spatial patterns and generate new spatial configurations with similar semantics of perception and navigation.
keywords Machine Learning; Artificial Intelligence; Generative Design
series CAADRIA
email immanuel.koh@epfl.ch
full text file.pdf (23,677,625 bytes)
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