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

• reinforcement learning
• evolutionary machine learning algorithms
• functional programming, especially using Haskell
• ways to incoporate a more formal (functional) view into everyday software such as operating systems (NixOS) or window managers (XMonad)
• free software

• Towards a Pittsburgh-Style LCS for Learning Manufacturing Machinery Parametrizations Michael Heider, David Pätzel and Jörg Hähner. 2020. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). ACM, New York, NY, USA, 127–128. DOI: https://doi.org/10.1145/3377929.3389963 (PDF)
• XCS as a reinforcement learning approach to automatic test case prioritization Lukas Rosenbauer, Anthony Stein, Roland Maier, David Pätzel and Jörg Hähner. 2020. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). ACM, New York, NY, USA, 1798–1806. DOI: https://doi.org/10.1145/3377929.3398128 (PDF)
• An overview of LCS research from IWLCS 2019 to 2020 David Pätzel, Anthony Stein and Masaya Nakata. 2020. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). ACM, New York, NY, USA, 1782–1788. DOI: https://doi.org/10.1145/3377929.3398105 (PDF)
• 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)