Active Learning of Deterministic Timed Automata with Myhill-Nerode Style Characterization
نویسندگان
چکیده
Abstract We present an algorithm to learn a deterministic timed automaton (DTA) via membership and equivalence queries. Our is extension of the L* with Myhill-Nerode style characterization recognizable languages, which class languages by DTAs. first characterize Nerode-style congruence. Using it, we give smart teacher answering symbolic queries in addition With query, one can ask certain set words at time. prove that for any language, our learning returns DTA recognizing it. show how answer query finitely many also requires polynomial number exponential normal teacher. applied various benchmarks confirmed its effectiveness
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2023
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-37706-8_1