Towards Model-driven Fuzzification of Adaptive Systems Specification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453439" target="_blank" >RIV/00216208:11320/22:10453439 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5220/0010910800003119" target="_blank" >https://doi.org/10.5220/0010910800003119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010910800003119" target="_blank" >10.5220/0010910800003119</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Model-driven Fuzzification of Adaptive Systems Specification

Popis výsledku v původním jazyce

The position paper outlines a method transforming adaptation rules in a self-adaptive system to a machine learning problem using neural networks. This makes it possible to endow a self-adaptive system with the possibility to learn. At the same time, by controlling the degree to which this transformation is done, one can scale the tradeoff between learning capacity and uncertainty in the self-adaptive system. The paper elaborates this process as a model transformation pipeline. The pipeline starts with a model capturing the strict adaptation rules. Then it is followed by multiple steps in which the strict rules are gradually fuzzified by well-defined transformations. The last model transformation in the pipeline transforms the fuzzified rules to a neural network that can be trained using the traditional stochastic gradient descent method. We briefly showcase this using two examples from the area of collective adaptive systems.

Název v anglickém jazyce

Towards Model-driven Fuzzification of Adaptive Systems Specification

Popis výsledku anglicky

The position paper outlines a method transforming adaptation rules in a self-adaptive system to a machine learning problem using neural networks. This makes it possible to endow a self-adaptive system with the possibility to learn. At the same time, by controlling the degree to which this transformation is done, one can scale the tradeoff between learning capacity and uncertainty in the self-adaptive system. The paper elaborates this process as a model transformation pipeline. The pipeline starts with a model capturing the strict adaptation rules. Then it is followed by multiple steps in which the strict rules are gradually fuzzified by well-defined transformations. The last model transformation in the pipeline transforms the fuzzified rules to a neural network that can be trained using the traditional stochastic gradient descent method. We briefly showcase this using two examples from the area of collective adaptive systems.

Druh

D - Stať ve sborníku

CEP obor

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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

Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - MODELSWARD

ISBN

978-989-758-550-0

ISSN

2184-4348

e-ISSN

—

Počet stran výsledku

8

Strana od-do

336-343

Název nakladatele

SciTePress

Místo vydání

Neuveden

Místo konání akce

virtual

Datum konání akce

6. 2. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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