Active Learning of Decomposable Systems

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Samenvatting

Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systems- with a decomposable set of actions-that make learning in parts possible. The systems are themselves decomposable through nonintersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry.

Originele taal-2Engels
Titel2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering (FormaliSE)
UitgeverijAssociation for Computing Machinery, Inc
Aantal pagina's10
ISBN van elektronische versie9781450370714
DOI's
StatusGepubliceerd - 7 okt 2020
Evenement2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering, FormaliSE 2020 - Seoul, Zuid-Korea
Duur: 13 jul 2020 → …

Congres

Congres2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering, FormaliSE 2020
LandZuid-Korea
StadSeoul
Periode13/07/20 → …

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