Conference proceeding
Learning Symbolic Formulations in Design Optimization
Design Computing and Cognition '08, pp 533-552
01 Jan 2008
Abstract
This paper presents a learning and inference mechanism for unsupervised learning of semantic concepts from purely syntactical examples of design optimization formulation data. Symbolic design formulation is a tough problem from computational and cognitive perspectives, requiring domain and mathematical expertise. By conceptualizing the learning problem as a statistical pattern extraction problem, the algorithm uses previous design experiences to learn design concepts. It then extracts this learnt knowledge for use with new problems. The algorithm is knowledge-lean, needing only the mathematical syntax of the problem as input, and generalizes quickly over a very small training data set. We demonstrate and evaluate the method on a class of hydraulic cylinder design problems.
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Details
- Title
- Learning Symbolic Formulations in Design Optimization
- Creators
- Somwrita Sarkar - The University of SydneyAndy Dong - The University of SydneyJohn S. Gero - George Mason University
- Contributors
- J S Gero (Editor) - George Mason UniversityA K Goel (Editor)
- Publication Details
- Design Computing and Cognition '08, pp 533-552
- Publisher
- Springer Nature
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000262457300028
- Scopus ID
- 2-s2.0-80052113011
- Other Identifier
- 991022157480204721