| id |
ecaade2021_252 |
| authors |
Kotov, Anatolii and Vukorep, Ilija |
| year |
2021 |
| title |
Gridworld Architecture Testbed |
| doi |
https://doi.org/10.52842/conf.ecaade.2021.1.037
|
| source |
Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 37-44 |
| summary |
Over centuries architects have developed frameworks of representation of the built surroundings in diverse types of drawings or models. With the rise of digital techniques, virtual models slowly replace these representation techniques but are still far from replicating the real world's ambiguity and complexity. This paper wants to address the representational problems of architecture combined with architecture-related AI systems and missing standardized tests for such systems. For this, we suggest a standardized computational testbed that can serve for developing, testing and benchmarking design solutions for abstracted architectural problems with various AI approaches in a game-like environment.Furthermore, this paper will discuss architectural problems' subdivision into atomic subtasks solvable by specific AI systems. Ideally, there is a waste number of possible architectural subtasks that can be applied. The paper presents some examples of possible architectural game strategies that abstractly deal with concepts of walls and borders, zones and connections. Although this paper mentions different Reinforcement Learning techniques, it is not focusing on fine-tuning the AI algorithms. It aims to help achieve automation of specific design workflow phases, then in the longer term to optimize and propose alternative design solutions and improve the architectural community's overall work. |
| keywords |
Gridworld Testbed; AI Aided Architecture; Benchmarking AI Algorithms |
| series |
eCAADe |
| email |
ilija.vukorep@b-tu.de |
| full text |
 file.pdf (707,545 bytes) |
| references |
Content-type: text/plain
|
Brockman, G, Cheung, V, Pettersson, L, Schneider, J, Schulman, J, Tang, J and Zaremba, W (2016)
 OpenAI Gym
, arXiv:1606.01540 [cs], /, p. /
|
|
|
|
|
Chaillou, S (2019)
AI + Architecture: Towards a New Approach
, Harvard University
|
|
|
|
|
Chan, CS (1990)
Cognitive Processes in Architectural Design Problem Solving
, Design Studies - DESIGN STUD, 11, pp. 60-80
|
|
|
|
|
Cobbe, K, Klimov, O, Hesse, C, Kim, T and Schulman, J (2019)
Quantifying Generalization in Reinforcement Learning
, arXiv:1812.02341 [cs, stat], /, p. /
|
|
|
|
|
López Méndez, MDC, González Arrieta, A, Queiruga Dios, M, Hernández Encinas, A and Queiruga-Dios, A (2017)
Minecraft as a Tool in the Teaching-Learning Process of the Fundamental Elements of Circulation in Architecture
, International Joint Conference SOCO'16-CISIS'16-ICEUTE'16, Cham, pp. 728-735
|
|
|
|
|
Radford, A, Kim, JW, Hallacy, C, Ramesh, A, Goh, G, Agarwal, S, Sastry, G, Askell, A, Mishkin, P, Clark, J, Krueger, G and Sutskever, I (2021)
Learning Transferable Visual Models From Natural Language Supervision
, arXiv:2103.00020 [cs], /, p. /
|
|
|
|
|
Salge, C and Polani, D (2013)
Changing the Environment based on Intrinsic Motivation
, arXiv:1310.3692 [nlin], /, p. /
|
|
|
|
|
Salge, C, Green, MC, Canaan, R, Skwarski, F, Fritsch, R, Brightmoore, A, Ye, S, Cao, C and Togelius, J (2020)
The AI Settlement Generation Challenge in Minecraft
, KI - Künstliche Intelligenz, 34(1), pp. 19-31
|
|
|
|
|
Schrittwieser, J, Antonoglou, I, Hubert, T, Simonyan, K, Sifre, L, Schmitt, S, Guez, A, Lockhart, E, Hassabis, D, Graepel, T, Lillicrap, T and Silver, D (2020)
Mastering Atari, Go, chess and shogi by planning with a learned model
, Nature, 588, pp. 604-609
|
|
|
|
|
Szegedy, C, Liu, W, Jia, Y, Sermanet, P, Reed, S, Anguelov, D, Erhan, D, Vanhoucke, V and Rabinovich, A (2014)
Going Deeper with Convolutions
, arXiv.org
|
|
|
|
|
Vukorep, I and Kotov, A (2021)
Machine learning in architecture
, As, IA and Basu, PB (eds), The Routledge Companion to Artificial Intelligence in Architecture, Taylor & Francis, pp. 93-109
|
|
|
|
|
Yu, T, Quillen, D, He, Z, Julian, R, Hausman, K, Finn, C and Levine, S (2020)
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
, Conference on Robot Learning, pp. 1094-1100
|
|
|
|
|
Zhu, J, Park, T, Isola, P and Efros, AA (2017)
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
, 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242-2251
|
|
|
|
|
Zwierzycki, M (2020)
On AI Adoption Issues in Architectural Design - Identifying the issues based on an extensive literature review.
, Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 515-524
|
|
|
|
| last changed |
2022/06/07 07:51 |
|
