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- W3161865814 abstract "Learning finite automata has been a popular topic. Shuffle has been applied in information systems. Since shuffle introduced into finite automata makes the membership problem NP-hard, and there are no learning algorithms for finite automata supporting shuffle so far, it is an essential work to devise effective and precise algorithms for learning finite automata supporting shuffle. In this paper, finite automata are learned from sets of positive samples. First, we define finite automata with shuffle (FA(&)s), for which both the uniform and the non-uniform membership problem are decidable in polynomial time. Then, we learn an FA(&) from a given finite sample step by step. Our algorithm can ensure that the learned FA(&) is a precise representation of the given finite sample. Experimental results demonstrate that, FA(&) is more efficient in membership checking, and our algorithm can obtain a more concise automaton." @default.
- W3161865814 created "2021-05-24" @default.
- W3161865814 creator A5058268034 @default.
- W3161865814 date "2021-01-01" @default.
- W3161865814 modified "2023-09-26" @default.
- W3161865814 title "Learning Finite Automata with Shuffle" @default.
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- W3161865814 doi "https://doi.org/10.1007/978-3-030-75765-6_25" @default.
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