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Beyond EDSM

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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

In this paper, we analyze the effectiveness of a leading finite state automaton (FSA) induction algorithm, windowed evidence driven state merging (W-EDSM). W-EDSM generates small automata that correctly label a given set of positive and a given set of negative example strings defined by a regular (Type 3) language. In particular, W-EDSM builds a prefix tree for the exemplars which is then collapsed into a FSA. This is done by selecting nodes to merge based on a simple heuristic until no more merges are possible. Our experimental results show that the heuristic used works well for later merges, but not very well for early merges. Based on this observation, we are able to make a small modification to W-EDSM which improves the performance of the algorithm by 27% and suggest other avenues for futher enhancement.

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References

  1. J. E. Hopcroft and J. D. Ullman, Formal Languages And Their Relation to Automata. Reading, Mass.: Addison-Wesley, 1969.

    MATH  Google Scholar 

  2. E. Gold, “Complexity of automaton identification from given data,” Information and Control, vol. 37, pp. 302–320, 1978.

    Article  MATH  MathSciNet  Google Scholar 

  3. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth, “Occam’s razor,” Information Processing Letters, vol. 24, pp. 377–380, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  4. B. A. Trakhtenbrot and Y. M. Barzdin, Finite Automata. Amsterdam: North-Holland, 1973.

    MATH  Google Scholar 

  5. K. J. Lang, “Random DFA’s can be approximately learned from sparse uniform examples,” in Proceedings of the Fifth ACM Workshop on Computational Learning Theory, (New York, N.Y.), pp. 45–52, ACM, 1992.

    Google Scholar 

  6. K. J. Lang, B. A. Pearlmutter, and R. A. Price, “Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm,” in Proc. 4th International Colloquium on Grammatical Inference-ICGI’ 98, vol. 1433, pp. 1–12, Springer-Verlag, 1998.

    Google Scholar 

  7. H. Juille and J. B. Pollack, “A sampling-based heuristic for tree search applied to grammar induction,” in Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98) Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98), (Madison, Wisconsin, USA), AAAI Press Books, 26–30 1998.

    Google Scholar 

  8. K. Lang, “Evidence-driven state merging with search,” 1998.

    Google Scholar 

  9. A. L. Oliveira and J. P. M. Silva, “Efficient search techniques for the inference of minimum size finite automata,” in String Processing and Information Retrieval, pp. 81–89, 1998.

    Google Scholar 

  10. J. M. Pena and A. L. Oliveira, “A new algorithm for the reduction of incompletely specified finite state machines,” in Proc. of the ACM/IEEE International Conference on Computer Aided Design, (San Jose), pp. 482–489, IEEE Computer Society Press, 1998.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Guelph Natural Computation Group Dept. of Computing and Information Science, University of Guelph, Guelph, ON, N1G 2W1, Canada

    Orlando Cicchello & Stefan C. Kremer

Authors
  1. Orlando Cicchello
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  2. Stefan C. Kremer
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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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© 2002 Springer-Verlag Berlin Heidelberg

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Cicchello, O., Kremer, S.C. (2002). Beyond EDSM. 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_4

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  • DOI: https://doi.org/10.1007/3-540-45790-9_4

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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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