Learning Symbolic Formulations in Design Optimization Somwrita Sarkar and Andy Dong University of Sydney, Australia John S. Gero George Mason University, USA 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. Motivation Design formulation and reformulation significantly affect the results of any automated optimization exercise, and is a difficult problem from both computational and cognitive perspectives. It is a difficult problem for many reasons – there is no known formal process that takes an abstract set of design requirements as input and produces a symbolic mathematical design model as output [1]; It is knowledge intensive and requires both domain and mathematical expertise from designers; the numbers of variables, parameters, objectives and constraints in a problem often exceed © Springer Science + Business Media B.V. 2008 J.S. Gero and A.K. Goel (eds.), Design Computing and Cognition ’08, 533