Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm

  • Kevin J. Lang
  • Barak A. Pearlmutter
  • Rodney A. Price
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1433)

Abstract

This paper first describes the structure and results of the Abbadingo One DFA Learning Competition. The competition was designed to encourage work on algorithms that scale well—both to larger DFAs and to sparser training data. We then describe and discuss the winning algorithm of Rodney Price, which orders state merges according to the amount of evidence in their favor. A second winning algorithm, of Hugues Juillé, will be described in a separate paper.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    B. Trakhtenbrot and Ya. Barzdin'. (1973) Finite Automata: Behavior and Synthesis. North-Holland Publishing Company, Amsterdam.Google Scholar
  2. 2.
    D. Angluin. (1978) On the Complexity of Minimum Inference of Regular Sets. Information and Control, Vol. 39, pp. 337–350.MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    L. Veelenturf. (1978) Inference of Sequential Machines from Sample Computations. IEEE Transactions on Computers, Vol. 27, pp. 167–170.MATHMathSciNetGoogle Scholar
  4. 4.
    M. Kearns and L. Valiant. (1989) Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. STOC-89.Google Scholar
  5. 5.
    L. Pitt and M. Warmuth. (1989) The Minimum DFA Consistency Problem Cannot be Approximated Within any Polynomial. STOC-89.Google Scholar
  6. 6.
    Kevin J. Lang. 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, July 1992.Google Scholar
  7. 7.
    J. Oncina and P. Garcia. Inferring Regular Languages in Polynomial Updated Time. In Pattern Recognition and Image Analysis. pp. 49–61, World Scientific, 1992.Google Scholar
  8. 8.
    Yoav Freund, Michael Kearns, Dana Ron, Ronitt Rubinfeld, Robert Schapire, and Linda Sellie. Efficient Learning of Typical Finite Automata from Random Walks, STOC-93, pp. 315–324.Google Scholar
  9. 9.
    P. Dupont, L. Miclet, and E. Vidal. What is the search space of the regular inference? In Proceedings of the International Colloquium on Grammatical Inference ICGA-94, Lecture Notes in Artificial Intelligence 862, pp. 25–37, Springer-Verlag, 1994.Google Scholar
  10. 10.
    C. de la Higuera, J. Oncina, and E. Vidal. Identification of DFA: Data-Dependent Versus Data-Independent Algorithms. In Proceedings of the International Colloquium on Grammatical Inference ICGA-96 Lecture Notes in Artificial Intelligence 1147, pp. 313–325, Springer-Verlag, 1996.Google Scholar
  11. 11.
    Joe Kilian and Kevin J. Lang. (1997) A Scheme for Secure Pass-Fail Tests. NECI Technical Note 97-016N.Google Scholar
  12. 12.
    Hugues Juillé and Jordan B. Pollack. (1998) SAGE: a Sampling-based Heuristic for Tree Search. Submitted to Machine Learning.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Kevin J. Lang
    • 1
  • Barak A. Pearlmutter
    • 2
  • Rodney A. Price
    • 3
  1. 1.NEC Research InstitutePrinceton
  2. 2.Comp Sci Dept, FEC 313Univ of New MexicoAlbuquerque
  3. 3.EmtexMilton KeynesEngland

Personalised recommendations