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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: [email protected] and Philip LeDuc, E-mail: [email protected]
Author for correspondence: Jonathan Cagan, E-mail: [email protected] and Philip LeDuc, E-mail: [email protected]
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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
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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References

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. 456–463.CrossRefGoogle 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. 162–171.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), 56–75.Google 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), 321–340.Google Scholar
Berwick, RC and Pilato, S (1987) Learning syntax by automata induction. Machine Learning 2(1), 9–38.Google 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.Google 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. 193–203.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.Google 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. 315–336.Google Scholar
Gips, J (1999) Computer implementation of shape grammars. In Proc. Workshop on Shape Computation, MIT. Accessed at http://www.shapegrammar.org/implement.pdfGoogle 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. 11–15.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. 828–838.Google 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. 363–381.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. 1–7.Google Scholar
Leach, P, Mealling, M and Salz, R (2005) A Universally Unique IDentifier (UUID) URN Namespace. The Internet Society, pp. 1–32.Google Scholar
Lee, YS and Wu, YC (2007) A robust multilingual portable phrase chunking system. Expert Systems with Applications 33(3), 590–599.Google 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), 273–290.Google Scholar
McKay, BD and Piperno, A (2014) Practical graph isomorphism, II. Journal of Symbolic Computation 60, 94–112.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.Google Scholar
Piazzalunga, U and Fitzhorn, P (1998) Note on a three-dimensional shape grammar interpreter. Environment and Planning B: Planning and Design 25(1), 11–30.Google 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), 031011-031011–11.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, 1–8.Google Scholar
Sánchez-Martínez, F and Pérez-Ortiz, JA (2010) Philipp Koehn, statistical machine translation. Machine Translation 24, 273–278.Google Scholar
Schmidt, LC and Cagan, J (1997) GGREADA: a graph grammar-based machine design algorithm. Research in Engineering Design 9(4), 195–213.Google 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. 39–57.Google Scholar
Schwenk, H (2012) Continuous space translation models for phrase-based statistical machine translation. COLING (Posters) (December), pp. 1071–1080.Google Scholar
Slisenko, AO (1982) Context-free grammars as a tool for describing polynomial-time subclasses of hard problems. Information Processing Letters 14(2), 52–56.CrossRefGoogle Scholar
Speller, TH, Whitney, D and Crawley, E (2007) Using shape grammar to derive cellular automata rule patterns. Complex Systems 17(1/2), 79–102.Google Scholar
Stiny, G (1980) Introduction to shape and shape grammars. Environment and Planning B 7(3), 343–351.Google Scholar
Stiny, G and Mitchell, WJ (1978). The palladian grammar. Environment and planning B: Planning and Design 5(1), 5–18.Google Scholar
Stolcke, A and Omohundro, S (1994) Inducing probabilistic grammars by Bayesian model merging. In Grammatical Inference and Applications, pp. 106–118.Google 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), 657–670.Google 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. 235–240.CrossRefGoogle Scholar
Yue, K and Krishnamurti, R (2013) Tractable shape grammars. Environment and Planning B: Planning and Design 40(4), 576–594.Google Scholar