The following paper describes the design principles of the Karlruhe Brainstormers team for the RoboCup Simulator League. The basic motivation behind our approach is to broadly apply Machine Learning techniques. In particular; our longterm goal is to apply Reinforcement Learning techniques to autonomously learn team playing capabilities. This longterm goal determined the structure of the decision module; which has to choose between several available high-level moves based on evaluation functions. We plan to reach the final autonomously learning agent in several stages. The current version uses a hybrid decision module with both rule-based and learning components.