Abstract
We present provably correct interactive algorithms for learning regular grammars from positive examples and membership queries. A structurally complete set of strings from a language L(G) corresponding to a target regular grammar G implicitly specifies a lattice of finite state automata (FSA) which contains a FSA MG corresponding to G. The lattice is compactly represented as a version-space and MG is identified by searching the version-space using membership queries. We explore the problem of regular grammar inference in a setting where positive examples are provided intermittently. We provide an incremental version of the algorithm along with a set of sufficient conditions for its convergence.
Vasant Honavar is grateful to National Science Foundation (grant NSF IRI-9409580) and the John Deere Foundation for supporting his research. The authors would like to thank Professor Giora Slutzki for several helpful discussions related to this work.
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Parekh, R., Honavar, V. (1996). An incremental interactive algorithm for regular grammar inference. 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/BFb0033358
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DOI: https://doi.org/10.1007/BFb0033358
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