Abstract
The use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.
Original language | English |
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Pages (from-to) | 95-111 |
Number of pages | 17 |
Journal | Artificial Intelligence for Engineering, Design, Analysis and Manufacturing |
Volume | 8 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1994 |
Keywords
- Bridge Design
- Concept Formation
- Design
- Empirical Network
- Machine Learning
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence