Abstract
Recently, Augmented Regular Expressions (AREs) have been proposed as a formalism to describe and recognize a non-trivial class of context-sensitive languages (CSLs), that covers planar shapes with symmetries [1, 2]. AREs augment the expressive power of Regular Expressions (REs) by including a set of constraints, that involve the number of instances in a string of the operands of the star operations of an RE. A general method to infer AREs from string examples has been reported [2] that is based on a regular grammatical inference (RGI) step followed by a constraint induction process. This approach avoids the difficulty of learning context-sensitive grammars. In this paper, a specific method for learning AREs from positive examples is described, in which the RGI step is carried out by training a recurrent neural network for a prediction task [3] and extracting a DFA from the network dynamics [4]. The ARE learning method has been applied to the inference of a set of eight test CSLs, and good experimental results have been obtained.
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References
R. Alquézar and A. Sanfeliu, ”Augmented regular expressions: a formalism to describe, recognize, and learn a class of context-sensitive languages,” Research Report LSI-95-17-R, Universitat Politecnica de Catalunya, Spain (1995).
R. Alquézar and A. Sanfeliu, ”Recognition and learning of a class of context-sensitive languages described by augmented regular expressions,” Pattern Recognition, in press, (1996).
R. Alquézar and A. Sanfeliu, ”Inference and recognition of regular grammars by training recurrent neural networks to learn the next-symbol prediction task,” in Advances in Pattern Recognition and Applications, F.Casacuberta and A.Sanfeliu (eds.), World Scientific Pub., Singapore, 48–59, (1994).
R. Alquézar and A. Sanfeliu, ”A hybrid connectionist-symbolic approach to regular grammatical inference based on neural learning and hierarchical clustering,” in Grammatical Inference and Applications, R.C.Carrasco and J.Oncina (eds.), Springer-Verlag, Lecture Notes in Artificial Intelligence 862, 203–211, (1994).
A. Salomaa, Formal Languages, Academic Press, New York (1973).
S.M. Chou and K.S.Fu, ”Inference for transition network grammars,” Proc. Int. Joint Conf. on Pattern Recognition, 3, CA, 79–84 (1976).
D. Angluin, ”Finding patterns common to a set of strings,” J. Comput. System Science 21, 46–62 (1980).
Y. Takada, ”A hierarchy of language families learnable by regular language learners,” in Grammatical Inference and Applications, R.C.Carrasco and J.Oncina (eds.), Springer-Verlag, Lecture Notes in Artificial Intelligence 862, 16–24 (1994).
J. Gregor, ”Data-driven inductive inference of finite-state automata,” Int. J. of Pattern Recognition and Artificial Intelligence 8 (1), 305–322 (1994).
P. Dupont, ”Regular grammatical inference from positive and negative samples by genetic search: the GIG method,” in Grammatical Inference and Applications, R.C.Carrasco and J.Oncina (eds.), Springer-Verlag, LNAI 862, 236–245, (1994).
L. Miclet, ”Inference of regular expressions,” Proc. of the 3rd Int. Conf. on Pattern Recognition, 100–105, (1976).
Z. Kohavi, Switching and Finite Automata Theory, (2nd edition). Tata McGraw-Hill, New Delhi, India (1978).
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© 1996 Springer-Verlag Berlin Heidelberg
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Alquézar, R., Sanfeliu, A., Cueva, J. (1996). Learning of context-sensitive language acceptors through regular inference and constraint induction. In: Miclet, L., de la Higuera, C. (eds) Grammatical Interference: Learning Syntax from Sentences. ICGI 1996. Lecture Notes in Computer Science, vol 1147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033349
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DOI: https://doi.org/10.1007/BFb0033349
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