Generalization of Machine-Learning Adaptation in Ensemble-Based Self-adaptive Systems
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Generalization of Machine-Learning Adaptation in Ensemble-Based Self-adaptive Systems
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
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)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Software Architecture. ECSA 2022 Tracks and Workshops
ISBN
978-3-031-36888-2
ISSN
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e-ISSN
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Number of pages
16
Pages from-to
386-401
Publisher name
Springer-Verlag
Place of publication
Berlin
Event location
Prague, Czech Republic
Event date
Sep 19, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
001310761900026