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.

本文在我们的基于集成的组件模型 DEECo 中扩展了使用机器学习和优化启发式算法来建立和重新配置自主组件集合的能力。我们展示了如何在模型层次上捕捉这些概念，并举例说明这样的模型如何有益地应用于工业 4.0 环境中与访问控制相关的问题。我们认为将机器学习和优化启发式算法纳入现代智能系统是一个关键特性，使其能够在学习过程中适应环境的不确定性并优化其行为。