RESEARCH FOCI

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

Other academic activities

I’ve reviewed journal articles for I was one of the three elected members of the organization committee of the 23rd and 24th International Workshop on Learning Classifier Systems (IWLCS) which took place at the 2020 and 2021 Genetic and Evolutionary Computation Conference (GECCO). Since then, I’m also a program committee member and review papers submitted to the IWLCS.

CURRICULUM/VITAE

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

COURSES / TEACHING

(applied filters: semester: current | lecturers: David Pätzel | course types: Übung, Seminar)
name semester type
Seminar zu Selbstorganisation in verteilten Systemen summer semester 2022 Seminar
Übung zu Organic Computing II summer semester 2022 Übung
Seminar über Naturanaloge Algorithmen und Multi-Agenten Systeme summer semester 2022 Seminar

Publications

David Pätzel
2021 | 2020 | 2019 | 2018

2021

David Pätzel, Michael Heider and Alexander R. M. Wagner. 2021. An overview of LCS research from 2020 to 2021. DOI: 10.1145/3449726.3463173
PDF | BibTeX | RIS | DOI

Lukas Rosenbauer, David Pätzel, Anthony Stein and Jörg Hähner. 2021. Transfer learning for automated test case prioritization using XCSF. DOI: 10.1007/978-3-030-72699-7_43
BibTeX | RIS | URL | DOI

2020

David Pätzel, Anthony Stein and Masaya Nakata. 2020. An overview of LCS research from IWLCS 2019 to 2020. DOI: 10.1145/3377929.3398105
BibTeX | RIS | DOI

Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems.
PDF | BibTeX | RIS | URL

Michael Heider, David Pätzel and Jörg Hähner. 2020. Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. DOI: 10.1145/3377929.3389963
PDF | BibTeX | RIS | DOI

Lukas Rosenbauer, Anthony Stein, Roland Maier, David Pätzel and Jörg Hähner. 2020. XCS as a reinforcement learning approach to automatic test case prioritization. DOI: 10.1145/3377929.3398128
BibTeX | RIS | DOI

Lukas Rosenbauer, Anthony Stein, David Pätzel and Jörg Hähner. 2020. XCSF for automatic test case prioritization. DOI: 10.5220/0010105700490058
PDF | BibTeX | RIS | DOI

Lukas Rosenbauer, Anthony Stein, David Pätzel and Jörg Hähner. 2020. XCSF with experience replay for automatic test case prioritization. DOI: 10.1109/ssci47803.2020.9308379
BibTeX | RIS | DOI

2019

David Pätzel, Anthony Stein and Jörg Hähner. 2019. A survey of formal theoretical advances regarding XCS. DOI: 10.1145/3319619.3326848
BibTeX | RIS | DOI

2018

David Pätzel and Jörg Hähner. 2018. An algebraic description of XCS. DOI: 10.1145/3205651.3208248
BibTeX | RIS | DOI

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