loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Petr Hnětynka ; Martin Kruliš ; Michal Töpfer and Tomáš Bureš

Affiliation: Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic

Keyword(s): Collective Adaptive Systems, Machine Learning, Model-Driven, Meta-Model.

Abstract: 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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.216.124.8

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Hnětynka, P.; Kruliš, M.; Töpfer, M. and Bureš, T. (2023). Modeling Machine Learning Concerns in Collective Adaptive Systems. In Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - MODELSWARD; ISBN 978-989-758-633-0; ISSN 2184-4348, SciTePress, pages 55-62. DOI: 10.5220/0011693300003402

@conference{modelsward23,
author={Petr Hnětynka. and Martin Kruliš. and Michal Töpfer. and Tomáš Bureš.},
title={Modeling Machine Learning Concerns in Collective Adaptive Systems},
booktitle={Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - MODELSWARD},
year={2023},
pages={55-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011693300003402},
isbn={978-989-758-633-0},
issn={2184-4348},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - MODELSWARD
TI - Modeling Machine Learning Concerns in Collective Adaptive Systems
SN - 978-989-758-633-0
IS - 2184-4348
AU - Hnětynka, P.
AU - Kruliš, M.
AU - Töpfer, M.
AU - Bureš, T.
PY - 2023
SP - 55
EP - 62
DO - 10.5220/0011693300003402
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic