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Inferring Subclasses of Regular Languages Faster Using RPNI and Forbidden Configurations

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Grammatical Inference: Algorithms and Applications (ICGI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2484))

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Abstract

Many varieties of regular languages have characterizations in terms of forbidden-patterns of their accepting finite automata. The use of patterns while inferring languages belonging to those families through the RPNI-Lang algorithm help to avoid overgeneralization in the same way as negative samples do. The aim of this paper is to describe and prove the convergence of a modification of the RPNI-Lang algorithm that we call FCRPNI. Preliminary experiments done seem to show that the convergence when we use FCRPNI for some subfamilies of regular languages is achieved faster than when we use just the RPNI algorithm.

Work partially supported by the Spanish CICYT under contract TIC2000-1153

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References

  1. Angluin, D. Inductive Inference of Formal Languages from Positive Data. Inform and Control, pp. 117–135 (1980).

    Google Scholar 

  2. Angluin, D. Inference of Reversible Languages. Journal of the ACM, Vol 29-3. pp. 741–765 (1982).

    Article  MathSciNet  Google Scholar 

  3. Carrasco, R. and Oncina, J. Learning Stochastic Regular Grammars by means of a State Merging Method. In Grammatical Inference and Applications. R. Carrasco and J. Oncina (Eds.). LNAI 862. Springer-Verlag, pp. 139–152 (1994).

    Google Scholar 

  4. Cohen, J. Perrin D. and Pin J-E. On the expressive power of temporal logic. Journal of computer and System Sciences 46, pp 271–294 (1993).

    Article  MATH  MathSciNet  Google Scholar 

  5. Coste, F. and Nicolas J. How considering Incompatible State Mergings May Reduce the DFA induction Search Tree. In Grammatical Inference. V. Honavar and G. Slutzki (Eds.) LNAI 1433. Springer-Verlag, pp 199–210 (1998).

    Google Scholar 

  6. Eilenberg, S. Automata, Languages and Machines, Vol A and B (Academic Press, 1976)

    Google Scholar 

  7. García, P. Cano, A. and Ruiz, J. A comparative study of two algorithms for automata identification. In Grammatical Inference: Algorithms and Applications. A.L. Oliveira (Ed.) LNAI 189. Springer-Verlag, pp. 115–126 (2000).

    Google Scholar 

  8. García P. and Vidal E. Inference of k-Testable languages in the Stric Sense and Applications to Syntactic Pattern Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12/9, pp 920–925 (1990).

    Article  Google Scholar 

  9. Glaßer, C. Forbidden-Patterns and Word Extensions for Concatenation Hierarchies. Ph dissertation, Würzburg University, Germany, 2001.

    Google Scholar 

  10. Gold, M. Complexity of Automaton Identification from Given Data. Information and Control 37, pp 302–320 (1978).

    Article  MATH  MathSciNet  Google Scholar 

  11. de la Higuera, C. Oncina, J. and Vidal, E. Data dependant vs data independant algorithms. In Grammatical Inference: Learning Syntax from Sentences. L. Miclet and C. de la Higuera (Eds.). LNAI 1147. Springer-Verlag, pp. 313–325 (1996).

    Google Scholar 

  12. Hopcroft, J. and Ullman, J. Introduction to Automata Theory, Languages and Computation. Addison-Wesley (1979).

    Google Scholar 

  13. Juillé, H. and Pollack J. A Stochastic Search Approach to Grammar Induction. In Grammatical Inference. V. Honavar and G. Slutzki (Eds.) LNAI 1433. Springer-Verlag, pp 126–137 (1998).

    Google Scholar 

  14. Lang, K. J. Random DFA’s can be Approximately Learned from Sparse Uniform Examples. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, pp 45–52. (1992).

    Google Scholar 

  15. Lang, K.J., Pearlmutter B.A. and Price R.A. Results on the Abbadingo One DFA Learning Competition and a New Evidence-Driven State Merging Algorithm In Grammatical Inference. V. Honavar and G. Slutzki (Eds.) LNAI 1433. Springer-Verlag, pp 1–12 (1998).

    Google Scholar 

  16. Oncina, J. and García, P. Inferring Regular Languages in Polynomial Updated Time. In Pattern Recognition and Image Analysys. Pérez de la Blanca, Sanfeliú and Vidal (Eds.) World Scientific. (1992).

    Google Scholar 

  17. Pin, J. Varieties of formal languages. Plenum. (1986).

    Google Scholar 

  18. Ruiz, J. and García, P. Learning k-piecewise testable languages from positive data. In Grammatical Inference: Learning Syntax from Sentences. L. Miclet and C. de la Higuera (Eds.). LNAI 1147. Springer-Verlag, pp. 203–210 (1996).

    Google Scholar 

  19. Schmitz, H. The Forbidden-Pattern approach to Concatenation Hierarchies. Ph dissertation, Würzburg University, Germany, 2001.

    Google Scholar 

  20. Stolcke, A. and Omohundro, S. Inducing Probabilistic Grammars by Bayesian Model Merging. In Grammatical Inference and Applications. R. Carrasco and J. Oncina (Eds.). LNAI 862. Springer-Verlag, pp. 106–118 (1994).

    Google Scholar 

  21. Trakhtenbrot B. and Barzdin Ya. Finite Automata: Behavior and Synthesis. North Holland Publishing Company. (1973).

    Google Scholar 

  22. Vidal, E. and Llorens, S. Using Knowledge to improve N-Gram Language Modelling through the MGGI Methodology. In Grammatical Inference: Learning Syntax from Sentences. L. Miclet and C. de la Higuera (Eds.). LNAI 1147. Springer-Verlag, pp. 179–190 (1996).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Depto. de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia, Spain

    Antonio Cano, José Ruiz & Pedro García

Authors
  1. Antonio Cano
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  2. José Ruiz
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  3. Pedro García
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Editor information

Editors and Affiliations

  1. Perot Systems Nederland B.V., Hoefseweg 1, 3821 AE, Amersfoort, The Netherlands

    Pieter Adriaans (Senior Research Advisor, Professor of Learning and Adaptive Systems) (Senior Research Advisor, Professor of Learning and Adaptive Systems)

  2. ILLC/Computation and Complexity Theory, Universiteit van Amsterdam, Plantage Muidergracht 24, 1018 TV, Amsterdam, The Netherlands

    Pieter Adriaans (Senior Research Advisor, Professor of Learning and Adaptive Systems) (Senior Research Advisor, Professor of Learning and Adaptive Systems)

  3. School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia

    Henning Fernau

  4. Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, 72076, Tübingen, Germany

    Henning Fernau

  5. FNWI/ILLC, Cognitive Systems and Information Processing Group, Universiteit van Amsterdam, Room B-5.39, Nieuwe Achtergracht 166, 1018 WV, Amsterdam, The Netherlands

    Menno van Zaanen

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Cano, A., Ruiz, J., García, P. (2002). Inferring Subclasses of Regular Languages Faster Using RPNI and Forbidden Configurations. In: Adriaans, P., Fernau, H., van Zaanen, M. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2002. Lecture Notes in Computer Science(), vol 2484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45790-9_3

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  • DOI: https://doi.org/10.1007/3-540-45790-9_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44239-4

  • Online ISBN: 978-3-540-45790-9

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