Learning Symbolic Formulations in Design Optimization

  • Somwrita Sarkar
  • Andy Dong
  • John S. Gero

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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Copyright information

© Springer Science+Business Media B.V 2008

Authors and Affiliations

  • Somwrita Sarkar
    • 1
  • Andy Dong
    • 1
  • John S. Gero
    • 2
  1. 1.University of SydneyAustralia
  2. 2.George Mason UniversityUSA

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