Gebäude: Bonifazius-Turm A
18. Etage, Raum 11
Tel: 06131 17-8206 oder 06131 39-38776
Mail:  daan.apeldoorn@uni-mainz.de 

Personal Details

• 2022: Doctoral degree in the field of Artificial Intelligence/Knowledge Representation from TU Dortmund University
• Since 2022: Reviewer for the Conference on Principles of Knowledge Representation and Reasoning (KR); further reviews, e.g., for the IEEE journal Transactions on Games.
• 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 releases 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)

Projects

Teaching

Johannes Gutenberg University Mainz:

• 2024: KI als Chance oder Risiko: von Grundlagen und Anwendungen bis hin zu Herausforderungen (Q+, gemeinsam mit weiteren Lehrenden)

IMBEI: 

• 2020: Course on Knowledge Base Extraction at IMBEI Summer School in Bioinformatics and High-Dimensional Statistics

Z Quadrat:

• 2022/2023: Courses in Artifical Intelligence and Robotics (with LEGO® Mindstorms)
• 2021/2022: Workshops/courses in Artificial Intelligence and Robotics (with LEGO® Mindstorms)
• 2019/2020 (selection): Workshops/courses in Artificial Intelligence, Robotics (with LEGO® Mindstorms), Games/Apps Programming (with EduGine C++), and others
• 2018/2019 (selection): Workshops/courses in Artificial Intelligence, Robotics (with LEGO® Mindstorms), Games Programming (with EduGine Java), and others

TU Dortmund University:

• Winter 2017/2018: Tutorial Darstellung, Verarbeitung und Erwerb von Wissen (Bachelor)
• Summer 2017: Tutorial Commonsense Reasoning (Master)
• Winter 2016/2017: Tutorial Darstellung, Verarbeitung und Erwerb von Wissen (Bachelor)
• Summer 2016: Tutorial Commonsense Reasoning (Master)
• Winter 2015/2016: Tutorial Darstellung, Verarbeitung und Erwerb von Wissen (Bachelor)
• Summer 2015: Tutorial Commonsense Reasoning (Master)

University of Koblenz-Landau:

• Winter 2014/2015: Tutorial Introduction to Web Science (Master, in English)

University of Hagen:

• Winter 2018/2019: Corrector for Wissensbasierte Systeme (Bachelor/Master)
• Summer 2018: Corrector for Wissensbasierte Systeme (Bachelor/Master)
• Summer 2011: Corrector for Entscheidungsmethoden in unternehmensweiten Softwaresystemen (Bachelor/Master)
• Winter 2010/2011: Corrector for Entscheidungsmethoden in unternehmensweiten Softwaresystemen (Bachelor/Master)

Co-Supervised Theses

• Entwicklung eines Agentenschwarms für die Verbesserung von Abläufen im Krankenhaus (together with Prof. Dr. Mehler, Master, finished)
• Integration zweier unterschiedlicher Agentenmodelle für Multi-Agenten-Szenarien mit Anwendung in der Logistik (together with Prof. Dr. Kern-Isberner, Master, finished)
• Erlernen von Wissensbasen mittels Reinforcement Learning und Neuronalen Netzen (together with Prof. Dr. Kern-Isberner, Master, finished)
• Lernen von Bayes-Netz-Strukturen als Grundlage für Agentenverhalten (together with Prof. Dr. Kern-Isberner, Bachelor, finished)
• Statistische Evaluation unterschiedlicher Repräsentationsformen für die Wissensextraktion aus Reinforcement Learning (together with Prof. Dr. Kern-Isberner, Bachelor, finished)

In the past, I was also supervising numerous projects for the German young researchers competition Jugend Forscht/Schüler Experimentieren.

Publications

• Thevapalan, A.*, Apeldoorn, D.*, Kern-Isberner, G., Meyer, R. G., Nietzke, M., Panholzer, T.: Comparison and Incorporation of Reasoning and Learning Approaches for Cancer Therapy Research. Studies in Health Technology and Informatics, 307:161–171, 2023. (url)
  (*shared first authorship) 

• Apeldoorn, D.: Comprehensible Knowledge Base Extraction for Learning Agents – Practical Challenges and Applications in Games. Dissertation at TU Dortmund University. Mainz, Aachen, 2023; Johannes Gutenberg University, Mainz, 2023. (url)

• Apeldoorn, D., Dockhorn, A.: Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning. IEEE Transactions on Games, 13(3):249–262, 2021. (url)

• Apeldoorn, D., Panholzer, T.: Automated Creation of Expert Systems with the InteKRator Toolbox. Studies in Health Technology and Informatics, 283:46–55, 2021. (url)

• Apeldoorn, D., Hadidi, L., Panholzer, T.: Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes. In: Chomphuwiset, P., Kim, J., Pawara, P. (eds.) Multi-disciplinary Trends in Artificial Intelligence – 14th International Conference, MIWAI 2021, Virtual Event, July 2–3, 2021, Proceedings, pp. 14–26. Springer, Cham, 2021. (url)

• Apeldoorn, D.: KI in der Schule – Teil 1: Einführung in die künstliche Intelligenz. LOG IN – Informatische Bildung und Computer in der Schule, 195/196:126–131, 2021.

• 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)

• 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. CEUR-WS.org, Aachen, 2018. (url)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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. CEUR-WS.org, Aachen, 2015. (url)

• 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)

• 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)

• 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.

• 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.