Attuning Adaptation Rules via a Rule-Specific Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453430" target="_blank" >RIV/00216208:11320/22:10453430 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-19759-8_14" target="_blank" >https://doi.org/10.1007/978-3-031-19759-8_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-19759-8_14" target="_blank" >10.1007/978-3-031-19759-8_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Attuning Adaptation Rules via a Rule-Specific Neural Network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Attuning Adaptation Rules via a Rule-Specific Neural Network

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust - popora důvěry pomocí dynamicky proměnlivého řízení přistupu k datům a zdrojům v systémech Průmyslu 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning 11th International Symposium, ISoLA 2022, Rhodes, Greece, October 22–30, 2022, Proceedings, Part III

ISBN

978-3-031-19758-1

ISSN

—

e-ISSN

1611-3349

Počet stran výsledku

16

Strana od-do

215-230

Název nakladatele

Springer

Místo vydání

Cham, Germany

Místo konání akce

Rhodes, Greece

Datum konání akce

22. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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