Generalization of Machine-Learning Adaptation in Ensemble-Based Self-adaptive Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10467589" target="_blank" >RIV/00216208:11320/23:10467589 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-36889-9_26" target="_blank" >https://doi.org/10.1007/978-3-031-36889-9_26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-36889-9_26" target="_blank" >10.1007/978-3-031-36889-9_26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Generalization of Machine-Learning Adaptation in Ensemble-Based Self-adaptive Systems

Popis výsledku v původním jazyce

Smart self-adaptive systems are nowadays commonly employed almost in any application domain. Within them, groups of robots, autonomous vehicles, drones, and similar automatons dynamically cooperate to achieve a common goal. An approach to model such dynamic cooperation is via autonomic component ensembles, which are dynamically formed groups of components. Forming ensembles is described via a set of constraints (e.g., form an ensemble of three drones closest to a target that have sufficient battery level to reach the target and stay there). Evaluating these constraints by traditional means (such as a SAT solver) is computationally demanding and does not scale for large systems. This paper proposes an approach for solving ensemble formations based on machine learning which may be relatively faster. The method trains the model on a small instance of the system governed by a computationally demanding algorithm and then adapts it for large instances thanks to the generalization properties of the machine learning model.

Název v anglickém jazyce

Generalization of Machine-Learning Adaptation in Ensemble-Based Self-adaptive Systems

Popis výsledku anglicky

Smart self-adaptive systems are nowadays commonly employed almost in any application domain. Within them, groups of robots, autonomous vehicles, drones, and similar automatons dynamically cooperate to achieve a common goal. An approach to model such dynamic cooperation is via autonomic component ensembles, which are dynamically formed groups of components. Forming ensembles is described via a set of constraints (e.g., form an ensemble of three drones closest to a target that have sufficient battery level to reach the target and stay there). Evaluating these constraints by traditional means (such as a SAT solver) is computationally demanding and does not scale for large systems. This paper proposes an approach for solving ensemble formations based on machine learning which may be relatively faster. The method trains the model on a small instance of the system governed by a computationally demanding algorithm and then adapts it for large instances thanks to the generalization properties of the machine learning model.

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Software Architecture. ECSA 2022 Tracks and Workshops

ISBN

978-3-031-36888-2

ISSN

—

e-ISSN

—

Počet stran výsledku

16

Strana od-do

386-401

Název nakladatele

Springer-Verlag

Místo vydání

Berlin

Místo konání akce

Prague, Czech Republic

Datum konání akce

19. 9. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

001310761900026