Visual Universitätsmedizin Mainz

Personal Details

  • Since 2019: Staff member at the Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI) at the University Medical Center of the Johannes Gutenberg University Mainz
  • Since 2018: Staff member at the Z Quadrat GmbH
  • 2017: Award Forum Young Top Researchers 2nd place
  • 2015-2018: Scientific staff member at the TU Dortmund University
  • 2014-2015: Scientific staff member at the University of Koblenz-Landau
  • Since 2014: Jury member of the German competition for young researchers Jugend forscht/Schüler experimentieren
  • 2014: MSc in Computer Science from the University of Hagen
  • 2014: Helmholtz-Teacher-Award
  • 2009-2014: Extracurricular staff member/teacher at the Otto-Schott-Gymnasium (secondary school) in Mainz
  • 2007-2009: Systems Engineer at the IMAGIN (Schweiz) AG
  • 2006: BSc in Computer Science from the University of Mainz (minor subject: Musicology)
  • 2005-2013: Co-founder of the music project Acoustique Parfum; several music publications on different record labels (e.g., Mole Listening Pearls)
  • 2002: Hasso-Plattner-Award for Software Systems Technology
  • 1999-2002: Developer at a Children Cancer Database at the University Hospital of Mainz

Research Interests

  • Multi-agent systems and simulations (e.g., with application in logistics)
  • Incorporation of symbolic and subsymbolic approaches with a focus on agent-based learning with knowledge extraction/integration
  • Methods of Artificial Intelligence and their application in the context of autonomous agents (e.g., in games)

In the past, I was also working on a project in the field of Software Engineering.


  • Dockhorn, A., Apeldoorn, D.: Forward Model Approximation for General Video Game Learning. In: Browne, C., Winands, M. H. M., Liu, J., Preuss, M. (eds.) Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18), pp. 425–432. IEEE, Piscataway, 2018. (url) (bibtex)
  • Apeldoorn, D., Kern-Isberner, G.: An Agent-Based Learning Approach for Finding and Exploiting Heuristics in Unknown Environments. In: Gordon, A. S., Miller, R., Turán, G. (eds.) Proceedings of the Thirteenth International Symposium on Commonsense Reasoning, London, UK, November 6-8, 2017. CEUR Workshop Proceedings, vol. 2052, paper 1., Aachen, 2018. (url) (bibtex)
  • Apeldoorn, D., Volz, V.: Measuring Strategic Depth in Games Using Hierarchical Knowledge Bases. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp. 9–16. IEEE, Piscataway, 2017. (url) (bibtex)
  • Krüger, C., Apeldoorn, D., Kern-Isberner, G.: Comparing Answer Set Programming and Hierarchical Knowledge Bases Regarding Comprehensibility and Reasoning Efficiency in the Context of Agents. In: Proceedings of the 30th International Workshop on Qualitative Reasoning (QR 2017) at International Joint Conference on Artificial Intelligence (IJCAI 2017) in Melbourne, Australia. Northwestern University, Evanston, Illinois, 2017. (url) (bibtex)
  • Apeldoorn, D., Kern-Isberner, G.: Towards an Understanding of What is Learned: Extracting Multi-Abstraction-Level Knowledge from Learning Agents. In: Rus, V., Markov, Z. (eds.) Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, pp. 764–767. AAAI Press, Palo Alto, California, 2017. (url) (bibtex)
  • Apeldoorn, D., Kern-Isberner, G.: When Should Learning Agents Switch to Explicit Knowledge? In: Benzmüller, C., Sutcliffe, G., Rojas, R. (eds.) GCAI 2016. 2nd Global Conference on Artificial Intelligence. EPiC Series in Computing, vol. 41, pp. 174–186. EasyChair Publications, 2016. (url) (bibtex)
  • Apeldoorn, D.: A Spatio-Temporal Multiagent Simulation Framework for Reusing Agents in Different Kinds of Scenarios. In: Müller, J. P., Ketter, W., Kaminka, G., Wagner, G., Bulling, N. (eds.) Multiagent System Technologies. LNAI, vol. 9433, pp. 79–97. Springer International Publishing, Switzerland, 2015. (url) (bibtex)
  • Apeldoorn, D.: Learning Rules for Cooperative Solving of Spatio-Temporal Problems. In: Beierle, C., Kern-Isberner, G., Ragni, M., Stolzenburg, F. (eds.) Proceedings of the 5th Workshop on Dynamics of Knowledge and Belief (DKB-2015) and the 4th Workshop KI & Kognition (KIK-2015) co-located with the 38th German Conference on Artificial Intelligence (KI-2015), Dresden, Germany, September 22, 2015. CEUR Workshop Proceedings, vol. 1444, pp. 5–15., Aachen, 2015. (url) (bibtex)
  • Apeldoorn, D.: AbstractSwarm – A Generic Graphical Modeling Language for Multi-Agent Systems. In: Klusch, M., Thimm, M., Paprzycki, M. (eds.) Multiagent System Technologies. LNCS, vol. 8076, pp. 180–192. Springer, Berlin Heidelberg, 2013. (url) (bibtex)
  • Apeldoorn, D.: Statistical Relational Learning in Dynamic Environments – An Agent-Based Approach to Dynamic Pathfinding Using Bayesian Logic Networks and ProbCog. In: Beierle, C., Kern-Isberner, G. (eds.) Informatik Berichte 361–09/2011: Evolving Knowledge in Theory and Applications – Proceedings of the 3rd Workshop on Dynamics of Knowledge and Belief (DKB 2011) at the 34th Annual Conference on Artificial Intelligence (KI-2011) in Berlin, pp. 61–71. FernUniversität in Hagen, Hagen, 2011. (url) (bibtex)
  • Apeldoorn, D., Heimbürger, H.: Method-oriented software development (MOSD) with the programming language C-mol – a new concept for improved Human Computer Interaction regarding the transfer of an idea to its realization. TESI 2005 Conference Proceedings, AIS II.2, Highbury Business, Kent, 2005. (bibtex)
  • Apeldoorn, D., Heimbürger, H.: Method-oriented software development (MOSD) with the programming language C-mol – A new concept for more efficient development and implementation of software systems. In: Gesellschaft für Informatik e.V. (ed.) Informatiktage 2003: Fachwissenschaftlicher Informatik-Kongress, pp. 103–106. Konradin Verlagsgruppe, Grasbrunn, 2004. (bibtex)