Compositional Learning for Interleaving Parallel Automata

Faezeh Labbaf, Jan Friso Groote, Hossein Hojjat, Mohammad Reza Mousavi

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)
28 Downloads (Pure)


Active automata learning has been a successful technique to learn the behaviour
of state-based systems by interacting with them through queries. In this paper, we develop a compositional algorithm for active automata learning in which systems comprising interleaving parallel components are learned compositionally. Our algorithm automatically learns the structure of systems while learning the behaviour of the components. We prove that our approach is sound and that it learns a maximal set of interleaving parallel components. We empirically evaluate the effectiveness of our approach and show that our approach requires significantly fewer numbers of input symbols and resets while learning systems. Our empirical evaluation is based on a large number of subject systems obtained from a case study in the automotive domain.
Original languageEnglish
Title of host publicationProceedings of the 26th International Conference on Foundations of Software Science and Computation Structures (FoSSaCS 2023)
EditorsOrna Kupferman, Pawel Sobocinski
Number of pages23
ISBN (Print)9783031308284
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13992 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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