# Ensemble-based modeling abstractions for modern self-optimizing systems

arXiv: https://arxiv.org/abs/2309.05823 · HTML: https://mlsanity.com/paper/2309.05823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Programming the Interactions of Collective Adaptive Systems by Relying on Attribute-based Communication](https://mlsanity.com/paper/1711.06092/) (2017)
- [On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems](https://mlsanity.com/paper/2103.10194/) (2021)
- [On the Power of Attribute-based Communication](https://mlsanity.com/paper/1602.05635/) (2016)

## Abstract (arXiv)

In this paper, we extend our ensemble-based component model DEECo with the capability to use machine-learning and optimization heuristics in establishing and reconfiguration of autonomic component ensembles. We show how to capture these concepts on the model level and give an example of how such a model can be beneficially used for modeling access-control related problem in the Industry 4.0 settings. We argue that incorporating machine-learning and optimization heuristics is a key feature for modern smart systems which are to learn over the time and optimize their behavior at runtime to deal with uncertainty in their environment.