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Conference papers

Angluin-Style Learning of NFA

Abstract : We introduce NL*, a learning algorithm for inferring non-deterministic finite-state automata using membership and equivalence queries. More specifically, residual finite-state automata (RFSA) are learned similarly as in Angluin's popular L* algorithm, which, however, learns deterministic finite-state automata (DFA). Like in a DFA, the states of an RFSA represent residual languages. Unlike a DFA, an RFSA restricts to prime residual languages, which cannot be described as the union of other residual languages. In doing so, RFSA can be exponentially more succinct than DFA. They are, therefore, the preferable choice for many learning applications. The implementation of our algorithms is applied to a collection of examples and confirms the expected advantage of NL* over L*.
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Contributor : Stefan Haar Connect in order to contact the contributor
Submitted on : Thursday, January 10, 2013 - 8:18:19 PM
Last modification on : Monday, January 24, 2022 - 9:35:25 PM


  • HAL Id : hal-00772636, version 1



Benedikt Bollig, Peter Habermehl, Carsten Kern, Martin Leucker. Angluin-Style Learning of NFA. Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Jul 2009, Pasadena, CA, USA, United States. pp.1004-1009. ⟨hal-00772636⟩



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