ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453434" target="_blank" >RIV/00216208:11320/22:10453434 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ACSOSC56246.2022.00033" target="_blank" >https://doi.org/10.1109/ACSOSC56246.2022.00033</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACSOSC56246.2022.00033" target="_blank" >10.1109/ACSOSC56246.2022.00033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components

Popis výsledku v původním jazyce

Machine learning has already proven itself in many scientific domains, most notably by improving classifications or value-predictions (regression). However, its wide deployment in the field of adaptive systems has yet to come. There are several reasons for this cautious adoption, such as the lack of sufficient data for training the models or reluctance to incorporate black-box models into the adaptation processes of existing systems. To gap these limitations, we are often required to perform detailed simulations to generate training data or verify the feasibility of adopting ML models in our systems. We present the ML-DEECo framework that should simplify the design of ML-enabled simulations, which removes repetitive code such as gathering specific data from the simulation, using these data to train models, and applying these models in the simulations as predictors. We also provide two case studies as an example and an evaluation of the usability of the proposed framework.

Název v anglickém jazyce

ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components

Popis výsledku anglicky

Machine learning has already proven itself in many scientific domains, most notably by improving classifications or value-predictions (regression). However, its wide deployment in the field of adaptive systems has yet to come. There are several reasons for this cautious adoption, such as the lack of sufficient data for training the models or reluctance to incorporate black-box models into the adaptation processes of existing systems. To gap these limitations, we are often required to perform detailed simulations to generate training data or verify the feasibility of adopting ML models in our systems. We present the ML-DEECo framework that should simplify the design of ML-enabled simulations, which removes repetitive code such as gathering specific data from the simulation, using these data to train models, and applying these models in the simulations as predictors. We also provide two case studies as an example and an evaluation of the usability of the proposed framework.

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

2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS COMPANION (ACSOS-C 2022)

ISBN

978-1-66545-142-0

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

66-69

Název nakladatele

IEEE COMPUTER SOC

Místo vydání

LOS ALAMITOS

Místo konání akce

virtual

Datum konání akce

19. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000886623600016