Learning Product Automata

Joshua Moerman*

*Corresponding author for this work

Research output: Working paper / PreprintPreprintAcademic


In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.
Original languageEnglish
PublisherCornell University - arXiv
Number of pages5
Publication statusPublished - May 2017
Externally publishedYes

Publication series

SeriesComputing Research Repository


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  • Learning Product Automata

    Moerman, J., 2019, Proceedings of Machine Learning Research. Unold, O., Dyrka, W. & Wieczorek, W. (eds.). PMLR, Vol. 93. p. 54-66 13 p.

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

    Open Access

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