Abstract
We consider inference of automata from given data. A classical problem is to find the smallest compatible automaton, i.e. the smallest automaton accepting all examples and rejecting all counter-examples. We study unambiguous automata (UFA) inference, an intermediate framework between the hard nondeterministic automata (NFA) inference and the well known deterministic automata (DFA) inference. The search space for UFA inference is described and original theoretical results on both the DFA and the UFA inference search space are given. An algorithm for UFA inference is proposed and experimental results on a benchmark with both deterministic and nondeterministic targets are provided showing that UFA inference outperforms DFA inference.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Abbadingo one (1998), http://abbadingo.cs.unm.edu/
Alquézar, R., Sanfeliu, A.: Incremental grammatical inference from positive and negative data using unbiased finite state automata. In: Shape, Structure and Pattern Recognition, Proc. Int. Workshop on Structural and Syntactic Pattern Recognition, SSPR 1994, Nahariya (Israel), pp. 291–300 (1995)
Coste, F., Fredouille, D.: Efficient ambiguity detection in C-NFA, a step toward inference of non deterministic automata. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 25–38. Springer, Heidelberg (2000)
Coste, F., Fredouille, D.: What is the search space for the inference of nondeterministic, unambiguous and deterministic automata? Technical report, IRISA (2003) (to appear), download http://www.irisa.fr/prive/dfredoui/down/report.ps.gz
Coste, F.: State merging inference of finite state classifiers. Technical Report INRIA/RR-3695, IRISA (September 1999)
Dietterich, T.G.: Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation 10(7), 1895–1923 (1998)
Denis, F., Lemay, A., Terlutte, A.: Learning regular languages using non deterministic finite automate. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 39–50. Springer, Heidelberg (2000)
Denis, F., Lemay, A., Terlutte, A.: Learning regular languages using RFSA. In: Abe, N., Khardon, R., Zeugmann, T. (eds.) ALT 2001. LNCS (LNAI), vol. 2225, pp. 348–363. Springer, Heidelberg (2001)
Dupont, P., Miclet, L., Vidal, E.: What is the search space of the regular inference? In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 25–37. Springer, Heidelberg (1994)
Davey, B., Priesley, A.: Introduction to lattices and order. Cambridge mathematical textbooks (1990)
Dupont, P.: Utilisation et apprentissage de modèles de langages pour la reconnaissance de la parole continue. PhD thesis, Ecole Nationale Supérieure des Télécommunications (1996)
Gold, E.M.: Complexity of automaton identification from given data. Information and Control 37, 302–320 (1978)
de la Higuera, C.: Characteristic sets for polynomial grammatical inference. Machine Learning 27, 125–138 (1997)
Lang, K.J.: Random dfa’s can be approximately learned from sparse uniform examples. In: 5th ACM workshop on Computation Learning Theorie, pp. 45 – 52 (1992)
Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. In: Honavar, V.G., Slutzki, G. (eds.) ICGI 1998. LNCS (LNAI), vol. 1433, pp. 1–12. Springer, Heidelberg (1998)
Oncina, J., García, P.: Inferring regular languages in polynomial update time. Pattern Recognition and Image Analysis, 49–61 (1992)
Pitt, L., Warmuth, M.: The minimum consistent DFA problem cannot be approximated within any polynomial. In: 21st ACM Symposium on Theory of Computing, pp. 421–444 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Coste, F., Fredouille, D. (2003). Unambiguous Automata Inference by Means of State-Merging Methods. In: Lavrač, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds) Machine Learning: ECML 2003. ECML 2003. Lecture Notes in Computer Science(), vol 2837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39857-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-39857-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20121-2
Online ISBN: 978-3-540-39857-8
eBook Packages: Springer Book Archive