Matthias Thimm
Prof. Dr. Matthias Thimm

Artificial Intelligence Group
Faculty of Mathematics and Computer Science
FernUniversität in Hagen, Germany
Other websites:
Phone: +49 (0) 2331 987-3004

About me

I am the head of the Artificial Intelligence Group at the Faculty of Mathematics and Computer Science of the FernUniversität in Hagen, Germany. I received my PhD degree from the university of Dortmund (Germany) in 2011 and my habilitation degree from the university of Koblenz-Landau (Germany) in 2016. My research focus is on formal methods of knowledge representation and artificial intelligence, both from a conceptual as well as algorithmic perspective. I am interested in formal models of argumentation, in particular with respect to algorithmic approaches, quantitative extensions, game theoretical aspects for application in multi-agent systems, the relationship of argumentation and belief revision, and inconsistency measurement. Further interests include probabilistic reasoning with incomplete and inconsistent information in propositional and first-order representations of knowledge.

Consultation hours

To get in touch personally, please use one of my open online consultation hours listed below. No appointment is required, but you may have to wait in the waiting room until called upon.

Next consultation hours:

  • July 29, 16:00-17:00 CEST
  • August 13, 16:00-17:00 CEST
  • August 20, 16:00-17:00 CEST
  • September 3, 16:00-17:00 CEST

The Zoom room for the consultation hours is


18 Apr 2024
The paper "Optimisation and Approximation in Abstract Argumentation: The Case of Stable Semantics" has been accepted for IJCAI 2024.

18 Apr 2024
Four papers got accepted for RATIO 2024:

  • Lars Bengel, Lydia Blümel, Tjitze Rienstra, Matthias Thimm: Argumentation-based Probabilistic Causal Reasoning
  • Jonas Klein, Isabelle Kuhlmann, Matthias Thimm: Cluster-Specific Rule Mining for Argumentation-Based Classification
  • Sandra Hoffmann, Isabelle Kuhlmann, Matthias Thimm: Enhancing Abstract Argumentation Solvers with Machine Learning-Guided Heuristics: A Feasibility Study
  • Kenneth Skiba, Matthias Thimm, Johannes P. Wallner: Ranking Transition-based Medical Recommendations using Assumption-based Argumentation

12 Sep 2023
The solvers harper++ and fargo-limited won all sub-tracks in the approximate track of the ICCMA 2023 competition.

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