Learning Realtime One-Counter Automata
arXiv:2110.09434 · doi:10.1007/978-3-031-30823-9_14
Abstract
We present a new learning algorithm for realtime one-counter automata. Our algorithm uses membership and equivalence queries as in Angluin's L* algorithm, as well as counter value queries and partial equivalence queries. In a partial equivalence query, we ask the teacher whether the language of a given finite-state automaton coincides with a counter-bounded subset of the target language. We evaluate an implementation of our algorithm on a number of random benchmarks and on a use case regarding efficient JSON-stream validation.
55 pages, 9 figures, submitted to TACAS 2022