| id |
caadria2021_308 |
| authors |
Wang, Dasong and Snooks, Roland |
| year |
2021 |
| title |
Intuitive Behavior - The Operation of Reinforcement Learning in Generative Design Processes |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 101-110 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.101
|
| summary |
The paper posits a novel approach for augmenting existing generative design processes to embed a greater level of design intention and create more sophisticated generative methodologies. The research presented in the paper is part of a speculative research project, Artificial Agency, that explores the operation of Machine Learning (ML) in generative design and robotic fabrication processes. By framing the inherent limitation of contemporary generative design approaches, the paper speculates on a heuristic approach that hybridizes a Reinforcement Learning based top-down evolutionary approach with bottom-up emergent generative processes. This approach is developed through a design experiment that establishes a topological field with intuitive global awareness of pavilion-scale design criteria. Theoretical strategies and technical details are demonstrated in the design experiment in regard to the translation of ML definitions within a generative design context as well as the encoding of design intentions. Critical reflections are offered in regard to the impacts, characteristics, and challenges towards the further development of the approach. The paper attempts to broaden the range and impact of Artificial Intelligence applications in the architectural discipline. |
| keywords |
Machine Learning; Generative Design Process; Multi-Agent Systems; Reinforcement Learning |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (10,437,477 bytes) |
| references |
Content-type: text/plain
|
Craig, R (1987)
 Flocks, herds and schools: A distributed behavioral model
, Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques
|
|
|
|
|
Henderson, P, Bachman, R, Precup, J and Meger, D (2018)
Deep Reinforcement Learning that Matters
, The Thirty-Second AAAI Conference on Artificial Intelligence(AAAI-18)
|
|
|
|
|
Holland, J (1992)
Complex Adaptive Systems
, The MIT Press
|
|
|
|
|
Leach, N and Snooks, R (eds) (2017)
Swarm Intelligence: Architectures of Multi-Agent Systems
, Tongji University Press, Shanghai
|
|
|
|
|
Lomas, A (2014)
Cellular Forms: an Artistic Exploration of Morphogenesis
, AISB-50
|
|
|
|
|
Snooks, R (2014)
Behavioral Formation: Multi-Agent Algorithmic Design Strategies
, Ph.D. Thesis, RMIT
|
|
|
|
|
Sutton, R and Barto, A (1998)
Reinforcement Learning: An Introduction
, The MIT Press
|
|
|
|
|
Wang, D and Snooks, R (2020)
Artificial Intuitions of Generative Design: An Approach based on Reinforcement Learning
, Proceedings of CDRF 2020
|
|
|
|
| last changed |
2022/06/07 07:58 |
|
