Emergent Induction of Deterministic Context-Free L-system Grammar

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)


L-system is a bio-inspired computational model to capture growth process of plants. This paper proposes a new noise-tolerant grammatical induction LGIC2 for deterministic context-free L-systems. LGIC2 induces L-system grammars from a transmuted string mY, employing an emergent approach in order to enforce its noise tolerance. In the method, frequently appearing substrings are extracted from mY to form grammar candidates. A grammar candidate is used to generate a string Z; however, the number of grammar candidates gets huge, meaning enormous computational cost. Thus, how to prune grammar candidates is vital here. We introduce a couple of techniques such as pruning by frequency, pruning by goodness of fit, and pruning by contractive embedding. Finally, several candidates having the strongest similarities between mY and Z are selected as the final solutions. Our experiments using insertion-type transmutation showed that LGIC2 worked very nicely, much better than an enumerative method LGIC1.


L-system plant modeling knowledge discovery grammatical induction noise tolerance 


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  1. 1.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to algorithms. MIT Press (1990)Google Scholar
  2. 2.
    Damasevicius, R.: Structural analysis of regulatory DNA sequences using grammar inference and support vector machine. Neurocomputing 73, 633–638 (2010)CrossRefGoogle Scholar
  3. 3.
    de la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognition 38, 1332–1348 (2005)CrossRefGoogle Scholar
  4. 4.
    Hjaltason, G.R., Samet, H.: Contractive embedding methods for similarity searching in metric spaces. Technical Report CS-TR-4102, Univ. of Maryland (2000)Google Scholar
  5. 5.
    Levenshtein, V.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)MathSciNetGoogle Scholar
  6. 6.
    McCormack, J.: Interactive evolution of L-system grammars for computer graphics modelling. In: Complex Systems: From Biology to Computation, pp. 118–130. ISO Press, Amsterdam (1993)Google Scholar
  7. 7.
    Nakano, R.: Error correction of enumerative induction of deterministic context-free L-system grammar. IAENG Int. Journal of Computer Science 40(1), 47–52 (2013)Google Scholar
  8. 8.
    Nakano, R., Suzumura, S.: Grammatical induction with error correction for deterministic context-free L-systems. In: Proc. of the World Congress on Engineering and Computer Science 2012 (WCECS 2012), pp. 534–538 (2012)Google Scholar
  9. 9.
    Nakano, R., Yamada, N.: Number theory-based induction of deterministic context-free L-system grammar. In: Proc. Int. Joint Conf. on Knowledge Discovery, Knowledge Engineering and Knowledge Management 2010, pp. 194–199 (2010)Google Scholar
  10. 10.
    Nevill-Manning, C.G.: Inferring sequential structure. Technical Report Doctoral Thesis, Univ. of Waikato (1996)Google Scholar
  11. 11.
    Prusinkiewicz, P., Hanan, J.: Lindenmayer systems, fractals, and plants. Springer, New York (1989)CrossRefMATHGoogle Scholar
  12. 12.
    Prusinkiewicz, P., Lindenmayer, A.: The algorithmic beauty of plants. Springer, New York (1990)CrossRefMATHGoogle Scholar
  13. 13.
    Schlecht, J., Barnard, K., Springgs, E., Pryor, B.: Inferring grammar-based structure models from 3d microscopy data. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Chubu UniversityKasugaiJapan

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