Angluin-Style Learning of NFA
نویسندگان
چکیده
This paper introduces 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 similar as in Angluin’s popular L algorithm, which however learns deterministic finite-state automata (DFA). As RFSA can be exponentially more succinct than DFA, RFSA are 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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