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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Sarkar, S., Dong, A., Gero, J.S. (2008). Learning Symbolic Formulations in Design Optimization. In: Gero, J.S., Goel, A.K. (eds) Design Computing and Cognition '08. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8728-8_28
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DOI: https://doi.org/10.1007/978-1-4020-8728-8_28
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