Feldotto M, Graffi K. Systematic evaluation of peer-to-peer systems using PeerfactSim.KOM. Concurrency and Computation: Practice and Experience. 2016;28(5):1655-1677. doi:10.1002/cpe.3716

Graf T, Platzner M. Using Deep Convolutional Neural Networks in Monte Carlo Tree Search. In: Computer and Games. ; 2016.

Graf T, Platzner M. Monte-Carlo Simulation Balancing Revisited. In: IEEE Computational Intelligence and Games. ; 2016.

Wiersema T, Platzner M. Verifying Worst-Case Completion Times for Reconfigurable Hardware Modules using Proof-Carrying Hardware. In: Proceedings of the 11th International Symposium on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC 2016). ; 2016:1--8. doi:10.1109/ReCoSoC.2016.7533910

Moritzer E, Hüttner M, Henning B, Webersen M. An Approach to Non-Destructive Testing of Aged Polymers. In: The Polymer Processing Society, ed. ; 2016.

Moritzer E, Hüttner M, Henning B, Webersen M. Molekularen Schäden auf der Spur. Kunststoffe. 2016;(4):94-96.

Moritzer E, Hüttner M, Henning B, Webersen M. Detecting Molecular Damage. Kunststoffe International. 2016;(4):43-45.

Moritzer E, Hüttner M, Henning B, Webersen M. Non-destructive characterization of hygrothermally aged polymers. In: Society of Plastics Engineers, ed. ; 2016.

Moritzer E, Hüttner M, Henning B, Webersen M. Ultraschallbasierte Charakterisierung von gealterten Polymeren. In: Institut für Wissenschaftliche Veröffentlichungen, ed. Jahresmagazin Kunststofftechnik 2016. ; 2016:2-7.

Webersen M, Hüttner M, Bause F, Moritzer E, Henning B. Zerstörungsfreie Charakterisierung des hydrothermischen Alterungsverhaltens von Polymeren. In: AMA Service GmbH, ed. ; 2016:683-688. doi:10.5162/sensoren2016/P6.4

Dewender M. Verifikation von Service Kompositionen mit Spin. Universität Paderborn; 2016.

Heinisch P. Verifikation von Service Kompositionen mit Prolog. Universität Paderborn; 2016.

Eikel M. Insider-Resistent Distributed Storage Systems. Universität Paderborn; 2016.

Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.

Hoffmann F, Hüllermeier E, Mikut R, eds.  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.; 2016.

Jasinska K, Dembczynski K, Busa-Fekete R, Klerx T, Hüllermeier E. Extreme F-measure maximization using sparse probability estimates . In: Balcan MF, Weinberger KQ, eds. Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA. ; 2016.

Melnikov V, Hüllermeier E. Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In: European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy. ; 2016:756-771.

Dembczynski K, Kotlowski W, Waegeman W, Busa-Fekete R, Hüllermeier E. Consistency of probalistic classifier trees. In: In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy. ; 2016:511-526.

Shabani A, Paul A, Platon R, Hüllermeier E. Predicting the electricity consumption of buildings: An improved CBR approach. In: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA. ; 2016:356-369.

Pfannschmidt K, Hüllermeier E, Held S, Neiger R. Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts. In: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands. Springer; 2016:450-461.