| 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 |
| doi |
https://doi.org/10.52842/conf.caadria.2019.2.421
|
| 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 |
|
| full text |
 file.pdf (23,677,625 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:51 |
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