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Learning symbolic formulations in design: Syntax, semantics, and knowledge reification

Published online by Cambridge University Press:  29 January 2010

Somwrita Sarkar ,
Andy Dong  and
John S. Gero
Somwrita Sarkar
Affiliation:
Design Lab, Faculty of Architecture, Design and Planning, University of Sydney, Sydney, Australia
Andy Dong
Affiliation:
Design Lab, Faculty of Architecture, Design and Planning, University of Sydney, Sydney, Australia
John S. Gero
Affiliation:
Volgenau School of Information Technology and Engineering, George Mason University, Arlington, Virginia, USA
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Abstract

An artificial intelligence (AI) algorithm to automate symbolic design reformulation is an enduring challenge in design automation. Existing research shows that design tools either require high levels of knowledge engineering or large databases of training cases. To address these limitations, we present a singular value decomposition (SVD) and unsupervised clustering-based method that performs design reformulation by acquiring semantic knowledge from the syntax of design representations. The development of the method was analogically inspired by applications of SVD in statistical natural language processing and digital image processing. We demonstrate our method on an analytically formulated hydraulic cylinder design problem and an aeroengine design problem formulated using a nonanalytic design structure matrix form. Our results show that the method automates various design reformulation tasks on problems of varying sizes from different design domains, stated in analytic and nonanalytic representational forms. The behavior of the method presents observations that cannot be explained by pure symbolic AI approaches, including uncovering patterns of implicit knowledge that are not readily encoded as logical rules, and automating tasks that require the associative transformation of sets of inputs to experiences. As an explanation, we relate the structure and performance of our algorithm with findings in cognitive neuroscience, and present a set of theoretical postulates addressing an alternate perspective on how symbols may interact with each other in experiences to reify semantic knowledge in design representations.

Keywords

Machine Learning in DesignPattern ExtractionSingular Value DecompositionSymbolic Problem ReformulationUnsupervised Clustering

Information

Type
Special Issue Articles
Information
AI EDAM , Volume 24 , Issue 1: Design Computing and Cognition , February 2010 , pp. 63 - 85
Copyright
Copyright © Cambridge University Press 2010

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