| id |
acadia20_272 |
| authors |
del Campo, Matias; Carlson, Alexandra; Manninger, Sandra |
| year |
2020 |
| title |
How Machines Learn to Plan |
| source |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 272-281. |
| doi |
https://doi.org/10.52842/conf.acadia.2020.1.272
|
| summary |
This paper strives to interrogate the abilities of machine vision techniques based on a family of deep neural networks, called generative adversarial neural networks (GANs), to devise alternative planning solutions. The basis for these processes is a large database of existing planning solutions. For the experimental setup of this paper, these plans were divided into two separate learning classes: Modern and Baroque. The proposed algorithmic technique leverages the large amount of structural and symbolic information that is inherent to the design of planning solutions throughout history to generate novel unseen plans. In this area of inquiry, aspects of culture such as creativity, agency, and authorship are discussed, as neural networks can conceive solutions currently alien to designers. These can range from alien morphologies to advanced programmatic solutions. This paper is primarily interested in interrogating the second existing but uncharted territory. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (5,571,091 bytes) |
| references |
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| last changed |
2023/10/22 12:06 |
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