Modeling Machine Learning Concerns in Collective Adaptive Systems:

Title:

Modeling Machine Learning Concerns in Collective Adaptive Systems:

Authors:

P. Hnětynka, M. Kruliš, M. Töpfer, T. Bureš

Publication:

Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering, Lisbon, Portugal

DOI:

Year:

2023

ISBN:

978-989-758-633-0

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.

BibTeX:

@inproceedings{hnetynka_modeling_2023,
    title = {{Modeling Machine Learning Concerns in Collective Adaptive Systems:}},
    author = {Hnětynka, Petr and Kruliš, Martin and Töpfer, Michal and Bureš, Tomáš},
    year = {2023},
    booktitle = {{Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering, Lisbon, Portugal}},
    doi = {10.5220/0011693300003402},
    isbn = {978-989-758-633-0},
    pages = {55--62},
}