Attuning Adaptation Rules via a Rule-Specific Neural Network

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Attuning Adaptation Rules via a Rule-Specific Neural Network

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

<a href="/en/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust – Enabling trust by fluid access control to data and physical resources in Industry 4.0 systems</a><br>

Continuities

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

Publication year

2022

Confidentiality

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

Article name in the collection

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

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e-ISSN

1611-3349

Number of pages

16

Pages from-to

215-230

Publisher name

Springer

Place of publication

Cham, Germany

Event location

Rhodes, Greece

Event date

Oct 22, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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