Modeling Machine Learning Concerns in Collective 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%3A10467804" target="_blank" >RIV/00216208:11320/23:10467804 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0011693300003402" target="_blank" >https://doi.org/10.5220/0011693300003402</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0011693300003402" target="_blank" >10.5220/0011693300003402</a>
Alternative languages
Result language
angličtina
Original language name
Modeling Machine Learning Concerns in Collective Adaptive Systems
Original language description
Collective adaptive systems (CAS) are systems composed of a large number of heterogeneous entities without central control that adapt their behavior to reach a common goal. Adaptation and collaboration in such systems are traditionally specified via a set of logical rules. Nevertheless, such rules are often too rigid and do not allow for the evolution of a system. Thus, recent approaches started with the introduction of machine learning (ML) methods into CAS. In the is paper, we present a model-driven approach showing how CAS, which employs ML methods for adaptation, can be modeled-on both the platform independent and specific levels. In particular, we define a meta-model for modeling CAS and a mapping of concepts defined in the meta-model to the Python framework.
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
Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering
ISBN
978-989-758-633-0
ISSN
2184-4348
e-ISSN
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Number of pages
8
Pages from-to
55-62
Publisher name
SciTePress
Place of publication
Neuveden
Event location
Lisabon, Portugalsko
Event date
Feb 19, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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