Learning Product Automata

Joshua Moerman

doi:10.48550/arXiv.1705.02850

Learning Product Automata

Joshua Moerman^*

^*Corresponding author for this work

Research output: Working paper / Preprint › Preprint

Abstract

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 language	English
Publisher	Cornell University - arXiv
Number of pages	5
DOIs	https://doi.org/10.48550/arXiv.1705.02850
Publication status	Published - May 2017
Externally published	Yes

Publication series

Series	Computing Research Repository
ISSN	2331-8422

Access to Document

10.48550/arXiv.1705.02850

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 proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

Cite this

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AB - 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.

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Learning Product Automata

Abstract

Publication series

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Fingerprint

Research output

Learning Product Automata

Cite this