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Efficient probabilistic grammar induction for design

Published online by Cambridge University Press:  09 May 2018

Mark E. Whiting Open the ORCID record for Mark E. Whiting [Opens in a new window] ,
Jonathan Cagan  and
Philip LeDuc
Mark E. Whiting
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Jonathan Cagan*
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Philip LeDuc*
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
*
Author for correspondence: Jonathan Cagan, E-mail: cagan@cmu.edu and Philip LeDuc, E-mail: prl@andrew.cmu.edu
Author for correspondence: Jonathan Cagan, E-mail: cagan@cmu.edu and Philip LeDuc, E-mail: prl@andrew.cmu.edu
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Abstract

The use of grammars in design and analysis has been set back by the lack of automated ways to induce them from arbitrarily structured datasets. Machine translation methods provide a construct for inducing grammars from coded data which have been extended to be used for design through pre-coded design data. This work introduces a four-step process for inducing grammars from un-coded structured datasets which can constitute a wide variety of data types, including many used in the design. The method includes: (1) extracting objects from the data, (2) forming structures from objects, (3) expanding structures into rules based on frequency, and (4) finding rule similarities that lead to consolidation or abstraction. To evaluate this method, grammars are induced from generated data, architectural layouts and three-dimensional design models to demonstrate that this method offers usable grammars automatically which are functionally similar to grammars produced by hand.

Keywords

Graph grammargrammar inductionshape grammar

Information

Type
Regular Articles
Information
AI EDAM , Volume 32 , Special Issue 2: Advances in Implemented Shape Grammars: Solutions and Applications , May 2018 , pp. 177 - 188
Copyright
Copyright © Cambridge University Press 2018 

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