Skip to main content Accessibility help
Hostname: page-component-56f9d74cfd-p4n5r Total loading time: 0.364 Render date: 2022-06-24T23:56:58.128Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

Article contents

Efficient probabilistic grammar induction for design

Published online by Cambridge University Press:  09 May 2018

Mark E. Whiting
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Jonathan Cagan*
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Philip LeDuc*
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Author for correspondence: Jonathan Cagan, E-mail: and Philip LeDuc, E-mail:
Author for correspondence: Jonathan Cagan, E-mail: and Philip LeDuc, E-mail:


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.

Regular Articles
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Ates, K and Zhang, K (2007) Constructing VEGGIE: machine learning for context-sensitive graph grammars. In Proceedings – International Conference on Tools with Artificial Intelligence, ICTAI, pp. 456463.Google Scholar
Babai, L (2015) Graph Isomorphism in Quasipolynomial Time. arXiv 7443327, 84.Google Scholar
Babai, L, Kantor, WM and Luks, EM (1983) Computational complexity and the classification of finite simple groups. In 24th Annual Symposium on Foundations of Computer Science (Sfcs 1983), pp. 162171.CrossRefGoogle Scholar
Balahur, A and Turchi, M (2014) Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language 28(1), 5675.CrossRefGoogle Scholar
Barnes, M and Finch, EL (2008) Collada-Digital Asset Schema Release 1.5.0, Specification. Clearlake Park, CA: Khronos Group.Google Scholar
Benrós, D, Hanna, S and Duarte, JP (2012) A generic shape grammar for the Palladian Villa, Malagueira house, and prairie house. Design Computing and Cognition ‘12’ 12(18), 321340.Google Scholar
Berwick, RC and Pilato, S (1987) Learning syntax by automata induction. Machine Learning 2(1), 938.CrossRefGoogle Scholar
Chau, HH, Chen, X, McKay, A, and de Pennington, A (2004) Evaluation of a 3D shape grammar implementation. In Gero, JS (ed.). Design Computing and Cognition ’04. Dordrecht: Springer.CrossRefGoogle Scholar
DeNero, J and Uszkoreit, J (2011) Inducing sentence structure from parallel corpora for reordering. In EMNLP 2011 – Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 193203.Google Scholar
Ding, Y and Palmer, M (2005) Machine translation using probabilistic synchronous dependency insertion grammars. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), vol 38 (June), pp. 541–48.Google Scholar
Fouss, F, Pirotte, A, Renders, JM and Saerens, M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3), 355–69.CrossRefGoogle Scholar
Gero, JS (1994) Towards a model of exploration in computer-aided design. In Gero, JS and Tyugu, E (eds). Formal Design Methods for Computer-Aided Design. Amsterdam: North-Holland, pp. 315336.Google Scholar
Gips, J (1999) Computer implementation of shape grammars. In Proc. Workshop on Shape Computation, MIT. Accessed at Scholar
Hagberg, AA, Schult, DA and Swart, PJ (2008) Exploring network structure, dynamics, and function using NetworkX. In Proceedings of the 7th Python in Science Conference (SciPy2008), pp. 1115.Google Scholar
Kang, U, Tong, H and Sun, J (2012) Fast random walk graph kernel. In Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 828838.CrossRefGoogle Scholar
Knuth, DE (1998) The Art of Computer Programming Volume 3. Sorting and Searching. Reading, MA: Addison Wesley, vol. 3, p. 829.Google Scholar
Königseder, C and Shea, K (2015) Analyzing generative design grammars. In Design Computing and Cognition ‘14, pp. 363381.Google Scholar
Kudo, T and Matsumoto, Y (2002) Japanese dependency analysis using cascaded chunking. In Proceeding of the 6th Conference on Natural language learning – COLING-02, vol. 20, pp. 17.Google Scholar
Leach, P, Mealling, M and Salz, R (2005) A Universally Unique IDentifier (UUID) URN Namespace. The Internet Society, pp. 132.Google Scholar
Lee, YS and Wu, YC (2007) A robust multilingual portable phrase chunking system. Expert Systems with Applications 33(3), 590599.CrossRefGoogle Scholar
McCormack, JP and Cagan, J (2002) Designing inner hood panels through a shape grammar based framework. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 16(4), 273290.CrossRefGoogle Scholar
McKay, BD and Piperno, A (2014) Practical graph isomorphism, II. Journal of Symbolic Computation 60, 94112.CrossRefGoogle Scholar
Mikolov, T, Le, QV and Sutskever, I (2013) Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168v1, 1–10.Google Scholar
Orsborn, S and Cagan, J (2009) Multiagent shape grammar implementation: automatically generating form concepts according to a preference function. Journal of Mechanical Design 131(12), 121007.CrossRefGoogle Scholar
Piazzalunga, U and Fitzhorn, P (1998) Note on a three-dimensional shape grammar interpreter. Environment and Planning B: Planning and Design 25(1), 1130.CrossRefGoogle Scholar
Rawson, K and Stahovich, TF (2009) Learning design rules with explicit termination conditions to enable efficient automated design. Journal of Mechanical Design, Transactions of the ASME 131(3), 031011-03101111.CrossRefGoogle Scholar
Rowe, C (1977) Mathematics of the ideal villa and other essays. Jae 31, 48.Google Scholar
Rozenberg, G (1997) Handbook of graph grammars and computing by graph transformation. Handbook of Graph Grammars 1, 18.Google Scholar
Sánchez-Martínez, F and Pérez-Ortiz, JA (2010) Philipp Koehn, statistical machine translation. Machine Translation 24, 273278.CrossRefGoogle Scholar
Schmidt, LC and Cagan, J (1997) GGREADA: a graph grammar-based machine design algorithm. Research in Engineering Design 9(4), 195213.CrossRefGoogle Scholar
Schnier, T and Gero, JS (1996) Learning genetic representations as alternative to hand-coded shape grammars. In Artificial Intelligence in Design ’96. Dordrecht: Springer, pp. 3957.CrossRefGoogle Scholar
Schwenk, H (2012) Continuous space translation models for phrase-based statistical machine translation. COLING (Posters) (December), pp. 10711080.Google Scholar
Slisenko, AO (1982) Context-free grammars as a tool for describing polynomial-time subclasses of hard problems. Information Processing Letters 14(2), 5256.CrossRefGoogle Scholar
Speller, TH, Whitney, D and Crawley, E (2007) Using shape grammar to derive cellular automata rule patterns. Complex Systems 17(1/2), 79102.Google Scholar
Stiny, G (1980) Introduction to shape and shape grammars. Environment and Planning B 7(3), 343351.CrossRefGoogle Scholar
Stiny, G and Mitchell, WJ (1978). The palladian grammar. Environment and planning B: Planning and Design 5(1), 518.CrossRefGoogle Scholar
Stolcke, A and Omohundro, S (1994) Inducing probabilistic grammars by Bayesian model merging. In Grammatical Inference and Applications, pp. 106118.CrossRefGoogle Scholar
Suh, NP (2001) Axiomatic Design: Advances and Applications. New York: Oxford University Press.Google Scholar
Talton, J, Yang, L, Kumar, R, Lim, M, Goodman, N and Měch, R (2012) Learning design patterns with Bayesian grammar induction. In Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology – UIST ’12, p. 63.Google Scholar
Trescak, T, Esteva, M and Rodriguez, I (2012) A shape grammar interpreter for rectilinear forms. CAD Computer Aided Design 44(7), 657670.CrossRefGoogle Scholar
Trescak, T, Rodriguez, I and Esteva, M (2009) General shape grammar interpreter for intelligent designs generations. In Proceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009, pp. 235240.CrossRefGoogle Scholar
Yue, K and Krishnamurti, R (2013) Tractable shape grammars. Environment and Planning B: Planning and Design 40(4), 576594.CrossRefGoogle Scholar
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Efficient probabilistic grammar induction for design
Available formats

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Efficient probabilistic grammar induction for design
Available formats

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Efficient probabilistic grammar induction for design
Available formats

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *