Abstract
The position paper outlines a method transforming adaptation rules in a self-adaptive system to a machine learning problem using neural networks. This makes it possible to endow a self-adaptive system with the possibility to learn.
At the same time, by controlling the degree to which this transformation is done, one can scale the tradeoff between learning capacity and uncertainty in the self-adaptive system. The paper elaborates this process as a model transformation pipeline.
The pipeline starts with a model capturing the strict adaptation rules. Then it is followed by multiple steps in which the strict rules are gradually fuzzified by well-defined transformations.
The last model transformation in the pipeline transforms the fuzzified rules to a neural network that can be trained using the traditional stochastic gradient descent method. We briefly showcase this using two examples from the area of collective adaptive systems.