Abstract
We introduce a new algorithm for sequential learning of Mealy automata by congruence generator extension (CGE). Our approach makes use of techniques from term rewriting theory and universal algebra for compactly representing and manipulating automata using finite congruence generator sets represented as string rewriting systems (SRS). We prove that the CGE algorithm correctly learns in the limit.
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Balcazar, J.L., Diaz, J., Gavalda, R.: Algorithms for learning finite automata from queries: a unified view. In: Advances in Algorithms, Languages and Complexity, pp. 53–72. Kluwer, Dordrecht (1997)
Bohlin, T., Jonsson, B.: Regular Inference for Communication Protocol Entities, Tech. Report 2008-024, Dept. of Information Technology, Uppsala University (2008)
Dershowitz, N., Jouannaud, J.-P.: Rewrite systems. In: van Leeuwen, J. (ed.) Handbook of Theoretical Computer Science. North Holland, Amsterdam (1990)
Incremental regular inference. In: Miclet, L., de la Higuera, C. (eds.) ICGI 1996. LNCS(LNAI), vol. 1147, pp. 222–237. Springer, Heidelberg (1996)
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)
Gold, E.M.: Language identification in the limit. Information and Control 10(5), 447–474 (1967)
Groce, A., Peled, D., Yannakakis, M.: Adaptive Model Checking. Logic Journal of the IGPL 14(5), 729–744 (2006)
Knuth, D.E., Bendix, P.: Simple word problems in universal algebras. In: Leech, J. (ed.) Computational Problems in Abstract Algebra, pp. 263–269. Pergamon Press, Oxford (1970)
Lang, K.J.: Random DFA’s can be approximately learned from sparse uniform examples. In: Proc. of 5th ACM workshop on Computational Learning Theory, pp. 45–52 (1992)
Meinke, K.: Automated Black-Box Testing of Functional Correctness using Function Approximation. In: Rothermel, G. (ed.) Proc. ACM SIGSOFT Int. Symp. on Software Testing and Analysis, ISSTA 2004. Software Engineering Notes, vol. 29 (4), pp. 143–153. ACM Press, New York (2004)
Meinke, K., Sindhu, M.: On the Correctness and Performance of the IID Incremental Learning Algorithm for DFA, technical report, School of Computer Science and Communication, Royal Institute of Technology, Stockholm (2010)
Meinke, K., Tucker, J.V.: Universal Algebra. In: Abramsky, S., Gabbay, D., Maibaum, T.S.E. (eds.) Handbook of Logic in Computer Science, vol. 1, pp. 189–411. Oxford University Press, Oxford (1993)
Oncina, J., Garcia, P.: Inferring regular languages in polynomial update time. In: Perez de la Blanca, N., Sanfeliu, A., Vidal, E. (eds.) Pattern Recognition and Image Analysis. Series in Machine Perception and Artificial Intelligence, vol. 1, pp. 49–61. World Scientific, Singapore (1992)
Parekh, R., Honavar, V.: Grammar inference, automata induction and language acquisition. In: Dale, Moisl, Somers (eds.) Handbook of Natural Language Processing. Marcel Dekker, New York
A solution of the syntactic induction-inference problem for regular languages. Computer languages 3, 53–64 (1978)
Parkeh, R.G., Nichitiu, C., Honavar, V.G.: A polynomial time incremental algorithm for regular grammar inference. In: Honavar, V.G., Slutzki, G. (eds.) ICGI 1998. LNCS (LNAI), vol. 1433, p. 37. Springer, Heidelberg (1998)
Peled, D., Vardi, M.Y., Yannakakis, M.: Black-box Checking. In: Wu, J., et al. (eds.) Formal Methods for Protocol Engineering and Distributed Systems, FORTE/PSTV, Beijing, pp. 225–240. Kluwer, Dordrecht (1999)
Porat, S., Feldman, J.: Learning automata from ordered examples. Machine Learning 7, 109–138 (1991)
Raffelt, H., Steffen, B., Margaria, T.: Dynamic Testing Via Automata Learning. In: Yorav, K. (ed.) HVC 2007. LNCS, vol. 4899, pp. 136–152. Springer, Heidelberg (2008)
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Meinke, K. (2010). CGE: A Sequential Learning Algorithm for Mealy Automata. In: Sempere, J.M., García, P. (eds) Grammatical Inference: Theoretical Results and Applications. ICGI 2010. Lecture Notes in Computer Science(), vol 6339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15488-1_13
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DOI: https://doi.org/10.1007/978-3-642-15488-1_13
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