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ORCIDDaan Apeldoorn
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2020 – today
- 2024
[c13]Daan Apeldoorn
, Alexander Dockhorn, Torsten Panholzer:
AbstractSwarm Multi-Agent Logistics Competition: Multi-Agent Collaboration for Improving A Priori Unknown Logistics Scenarios. GECCO Companion 2024: 1-2- [c12]Xenia Heilmann, Daan Apeldoorn, Konstantin Strauch, Bertil Schmidt, Timm Lilienthal, Torsten Panholzer:
Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data. GMDS 2024: 261-269 - 2023
- [b1]Daan Apeldoorn:
Comprehensible knowledge base extraction for learning agents: practical challenges and applications in games. Technical University of Dortmund, Germany, Verlag Mainz 2023, ISBN 978-3-95886-490-0, pp. 1-194 - [c11]Andre Thevapalan, Daan Apeldoorn, Gabriele Kern-Isberner, Ralf Georg Meyer, Mathias Nietzke, Torsten Panholzer:
Comparison and Incorporation of Reasoning and Learning Approaches for Cancer Therapy Research. GMDS 2023: 161-171 - 2021
- [j2]Daan Apeldoorn:
KI in der Schule - Teil 1: Einführung in die künstliche Intelligenz. LOG IN 41(1): 126-131 (2021) - [j1]Daan Apeldoorn, Alexander Dockhorn:
Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning. IEEE Trans. Games 13(3): 249-262 (2021) - [c10]Daan Apeldoorn, Torsten Panholzer:
Automated Creation of Expert Systems with the InteKRator Toolbox. GMDS 2021: 46-55 - [c9]Daan Apeldoorn, Lars Hadidi, Torsten Panholzer:
Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes. MIWAI 2021: 14-26
2010 – 2019
- 2018
- [c8]Alexander Dockhorn, Daan Apeldoorn:
Forward Model Approximation for General Video Game Learning. CIG 2018: 1-8 - 2017
- [c7]Daan Apeldoorn, Vanessa Volz:
Measuring strategic depth in games using hierarchical knowledge bases. CIG 2017: 9-16 - [c6]Daan Apeldoorn, Gabriele Kern-Isberner:
An Agent-Based Learning Approach for Finding and Exploiting Heuristics in Unknown Environments. COMMONSENSE 2017 - [c5]Daan Apeldoorn, Gabriele Kern-Isberner:
Towards An Understanding of What is Learned: Extracting Multi-Abstraction-Level Knowledge from Learning Agents. FLAIRS 2017: 764-767 - 2016
- [c4]Daan Apeldoorn, Gabriele Kern-Isberner:
When Should Learning Agents Switch to Explicit Knowledge? GCAI 2016: 174-186 - 2015
- [c3]Daan Apeldoorn:
Learning Rules for Cooperative Solving of Spatio-Temporal Problems. DKB/KIK@KI 2015: 5-15 - [c2]Daan Apeldoorn:
A Spatio-Temporal Multiagent Simulation Framework for Reusing Agents in Different Kinds of Scenarios. MATES 2015: 79-97 - 2013
- [c1]Daan Apeldoorn:
AbstractSwarm - A Generic Graphical Modeling Language for Multi-Agent Systems. MATES 2013: 180-192
Coauthor Index
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