Date Available
9-5-2019
Year of Publication
2019
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. Brent Harrison
Abstract
The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a method of floorplan generation using two machine learning models: a sequential model that generates rooms within the floorplan, and a graph-based model that finds adjacencies between generated rooms. Each of these models can be altered such that they are each capable of producing a floorplan independently; however, we find that the combination of these models outperforms each of its pieces, as well as a statistic-based approach.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2019.391
Recommended Citation
Goodman, Genghis, "A Machine Learning Approach to Artificial Floorplan Generation" (2019). Theses and Dissertations--Computer Science. 89.
https://uknowledge.uky.edu/cs_etds/89
