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Attuning Adaptation Rules via a Rule-Specific Neural Network

Bureš, Tomáš; Hnětynka, Petr ; Kruliš, Martin; Plášil, František; Khalyeyev, Danylo; Hahner, Sebastian ORCID iD icon 1; Seifermann, Stephan ORCID iD icon 1; Walter, Maximilian ORCID iD icon 1; Heinrich, Robert 1
1 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

There have been a number of approaches to employing neural networks (NNs) in self-adaptive systems; in many cases, generic NNs/deep learning are utilized for this purpose. When this approach is to be applied to improve an adaptation process initially driven by logical adaptation rules, the problem is that (1) these rules represent a significant and tested body of domain knowledge, which may be lost if they are replaced by an NN, and (2) the learning process is inherently demanding given the black-box nature and the number of weights in generic NNs to be trained. In this paper, we introduce the rule-specific Neural Network (rsNN) method that makes it possible to transform the guard of an adaptation rule into an rsNN, the composition of which is driven by the structure of the logical predicates in the guard. Our experiments confirmed that the black box effect is eliminated, the number of weights is significantly reduced, and much faster learning is achieved while the accuracy is preserved.

Postprint §
DOI: 10.5445/IR/1000152568
Veröffentlicht am 18.10.2023
DOI: 10.1007/978-3-031-19759-8_14
Zitationen: 2
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator ISBN: 978-3-031-19759-8
ISSN: 0302-9743
KITopen-ID: 1000152568
HGF-Programm 46.23.03 (POF IV, LK 01) Engineering Security for Mobility Systems
Erschienen in Leveraging Applications of Formal Methods, Verification and Validation. Proceedings. Vol. 3. Ed.: T. Margaria
Veranstaltung 11th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2022), Rhodos, Griechenland, 22.10.2022 – 30.10.2022
Verlag Springer Nature Switzerland
Seiten 215–230
Serie Lecture Notes in Computer Science ; 13703
Vorab online veröffentlicht am 17.10.2022
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