|since 2017||Research Assistant with the chair for Organic Computing|
|2015–2017||Master course in Computer Science at the University of Augsburg|
|2011–2015||Bachelor course in Computer Science and Multimedia at the University of Augsburg|
My main research is directed towards Learning Classifier Systems (especially XCS and its derivatives), a family of versatile evolutionary rule-based machine learning algorithms. One of the main arguments for their application is that they generate models that are more easily interpreted than the models generated by other learning systems such as artificial neural networks. However, we still don't yet fully understand in a formal way how LCS work, that is, which kinds of problems LCS are able to learn exactly and why they can learn them—which weakens the argument for them by quite a bit. Through my work, I want to develop a more formal understanding of LCS and finally prove several assumptions that were, up to now, only validated experimentally.
Other than that, I'm very interested in
- A survey of formal theoretical advances regarding XCS. David Pätzel, Anthony Stein, and Jörg Hähner. 2019. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), Manuel López-Ibáñez (Ed.). ACM, New York, NY, USA, 1295-1302. DOI: https://doi.org/10.1145/3319619.3326848 (PDF)
- An algebraic description of XCS. David Pätzel and Jörg Hähner. 2018. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 1434-1441. DOI: https://doi.org/10.1145/3205651.3208248 (PDF)