id |
caadria2024_442 |
authors |
Li, Jia-Rong, Chang, Teng-Wen, Chang, Ching-Chih, Huang, Hsin-Yi, Hong, Cheng-Chun and Chang, Ya-Chen |
year |
2024 |
title |
CocoBot: Developing a Consent Communication Tool Based on Co-Evolutionary Model |
source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 49–58 |
doi |
https://doi.org/10.52842/conf.caadria.2024.2.049
|
summary |
In architecture and interior design, consent communication is crucial for the collaboration between clients and designers. With clients often needing assistance articulating particular design requirements, this study introduces CocoBot (Collaborative Consensus Bot), which integrates artificial intelligence-generated content (AIGC) technology into the communication of the design collaboration. This tool facilitates collaborative evolution between clients and designers by transforming requirements into tangible images, enhancing communication efficiency and consensus formation. The co-evolutionary model is at the core of CocoBot, which stimulates interaction and consensus formation between clients and interior designers by extracting semantic information from the communication process and generating images. It utilizes visualization to mitigate linguistic ambiguity and subjectivity, facilitating consensus attainment. Through qualitative research and expert interviews, we validate the effectiveness of CocoBot in improving communication between clients and designers, particularly in addressing language uncertainties and ambiguities, ushering in a new collaborative mode for designers and clients. |
keywords |
co-evolutionary model, Artificial Intelligence Generated Content (AIGC), design communication, collaboration, visualization, consensus formation |
series |
CAADRIA |
email |
|
full text |
file.pdf (4,578,535 bytes) |
references |
Content-type: text/plain
|
Cheng, Y.-B., & Chang, T.-W. (2007)
Artificial intelligence in the creative industries: a review
, Proceedings of the 12th Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2007), Nanjing, China
|
|
|
|
Chuang, C. L., & Chien, S. F. (2021)
Facilitating architect-client communication in the pre-design phase
, Projections-Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021
|
|
|
|
Jensen, M. B., & Das, A. (2020)
Technologies and Techniques for Collaborative Robotics in Architecture: establishing a framework for human-robotic design exploration
, 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2020: Faculty of Architecture Chulalongkorn University
|
|
|
|
Kim, J., & Lee, J.-K. (2020)
Stochastic detection of interior design styles using a deep-learning model for reference images
, Applied Sciences, 10(20), 7299. https://www.mdpi.com/2076-3417/10/20/7299
|
|
|
|
Oppenlaender, J. (2023)
A taxonomy of prompt modifiers for text-to-image generation
, Behaviour & Information Technology, 1-14. https://doi.org/10.1080/0144929X.2023.2286532
|
|
|
|
Poon, J., & Maher, M. (1997)
AI Art in Architecture
, Proceedings of the Second Conference on Computer Aided Architectural Design Research in Asia, CAADRIA 1997
|
|
|
|
Zhang, J., Fukuda, T., Yabuki, N., & Li, Y. (2023)
Synthesizing Style-Similar Residential Facade from Semantic Labeling According to the User-Provided Example
, 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2023
|
|
|
|
Zhang, L., Rao, A., & Agrawala, M. (2023)
Adding conditional control to text-to-image diffusion models
, Proceedings of the IEEE/CVF International Conference on Computer Vision
|
|
|
|
Zhou, Y., & Park, H.-J. (2021)
Sketch with Artificial Intelligence (AI)
, 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2021
|
|
|
|
last changed |
2024/11/17 22:05 |
|