Active Learning of One-Clock Timed Automata Using Constraint Solving

Xu, Runqing; An, Jie; Zhan, Bohua

doi:10.1007/978-3-031-19992-9_16

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Cited by 5 publications

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Active Learning of Deterministic Timed Automata with Myhill-Nerode Style Characterization

Waga

2023

Lecture Notes in Computer Science

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We present an algorithm to learn a deterministic timed automaton (DTA) via membership and equivalence queries. Our algorithm is an extension of the L* algorithm with a Myhill-Nerode style characterization of recognizable timed languages, which is the class of timed languages recognizable by DTAs. We first characterize the recognizable timed languages with a Nerode-style congruence. Using it, we give an algorithm with a smart teacher answering symbolic membership queries in addition to membership and equivalence queries. With a symbolic membership query, one can ask the membership of a certain set of timed words at one time. We prove that for any recognizable timed language, our learning algorithm returns a DTA recognizing it. We show how to answer a symbolic membership query with finitely many membership queries. We also show that our learning algorithm requires a polynomial number of queries with a smart teacher and an exponential number of queries with a normal teacher. We applied our algorithm to various benchmarks and confirmed its effectiveness with a normal teacher.

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