Difficult problems like the prediction of future behavior of a system are usually solved by using domain knowledge. This knowledge comes with a certain expense which can be monetary costs or efforts to generate it. We want to decrease this cost while using state of the art machine learning and prediction methods. Our aim is to replace the domain knowledge and create a black-box solution that offers automatic reasoning and accurate predictions.

Our guiding example is packet scheduling optimization in Vehicle to Vehicle (V2V) communication. As evaluation, we compare the prediction quality of a labour-intense whitebox approach with the presented fully automated blackbox approach.