AALpy: An Active Automata Learning Library

Edi Muskardin, Bernhard Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler

    Research output: Conference proceeding/Chapter in Book/Report/Conference Paperpeer-review


    AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for
    deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in active automata learning,
    as well as on an intuitive and seamlessly integrated interface for learning
    automata characterizing real-world reactive systems. In this manuscript,
    we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance.
    Original languageEnglish
    Title of host publicationAutomated Technology for Verification and Analysis - 19th International Symposium, ATVA 2021
    Subtitle of host publication, Gold Coast, Australia, October 18-22, 2021, Proceedings
    Publication statusAccepted/In press - 18 Oct 2021


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