Active Learning of Decomposable Systems

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Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering (FormaliSE)
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450370714
DOIs
Publication statusPublished - 7 Oct 2020
Event2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering, FormaliSE 2020 - Seoul, Korea, Republic of
Duration: 13 Jul 2020 → …

Conference

Conference2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering, FormaliSE 2020
CountryKorea, Republic of
CitySeoul
Period13/07/20 → …

Keywords

  • Active learning
  • Decomposition

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