Using deep learning to replace domain knowledge

Complex 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. Within the evaluation, we compare the prediction quality of a labourintense whitebox approach with the presented fully-automated blackbox approach. To ease the measurement process we propose a framework design which allows easy exchange of predictors. The results show the successful design of our framework as well as superior accuracy of the black box approach.