![](bg8.png)
ment, approaches can be found in (D’Angelo et al.,
2018) or (Weyns and Iftikhar, 2019). Nevertheless,
they do not integrate ML techniques.
6 CONCLUSION
In the paper, we have presented an approach for the
formal modeling of machine learning concepts in col-
lective adaptive systems. We have presented the con-
cept of estimators, which are architectural-level ob-
jects providing predictions about a particular quantity,
and defined a meta-model for them. Also, we have
proposed a mapping of concepts defined by the meta-
model to the Python framework, which thus represents
a particular platform-specific model.
While the Python framework is fully functional,
our future work is twofold. Currently, we are working
on incorporating additional machine learning methods
to be used as estimators implementation. Additionally,
we plan to provide an automated transformation from
the platform-independent specifications to the Python
framework.
ACKNOWLEDGMENTS
This work has been partially supported by the Czech
Science Foundation project 20-24814J and also par-
tially supported by Charles University institutional
funding SVV 260588.
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