Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.https://doi.org/10.17619/UNIPB/1-1780  

Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., &; Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), 9–5.https://doi.org/10.1137/21M1412682 

Gevers, K., Schöppner, V., &; Hüllermeier, E. (2021). Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. International Institute of Welding, online.

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1.https://doi.org/10.1109/tpami.2021.3051276 

Mohr, F., Wever, M. D., Tornede, A., &; Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., &; Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108.

Tornede, T., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.

Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., &; Soltenborn, C. (2021). A Meta-Review on Artiû01cial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.

Hüllermeier, E., Mohr, F., Tornede, A., &; Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).

Mohr, F., &; Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.

Damke, C., &; Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares &; L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer.https://doi.org/10.1007/978-3-030-88942-5 

Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification.https://doi.org/10.17619/UNIPB/1-1302 

Hanselle, J. M., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.

Pfannschmidt, K., &; Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In Lecture Notes in Computer Science. Cham.https://doi.org/10.1007/978-3-030-58285-2_30 

Damke, C., Melnikov, V., &; Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan &; M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.

Bengs, V., &; Hüllermeier, E. (2020). Preselection Bandits. In International Conference on Machine Learning (pp. 778–787).

Bengs, V., &; Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. ArXiv:2011.00813.

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.

Hanselle, J. M., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.

Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020.https://doi.org/10.1007/978-3-030-66770-2_8 

Heid, S. H., Wever, M. D., &; Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.

El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., &; Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer.https://doi.org/10.1007/978-3-030-53552-0_22 

El Mesaoudi-Paul, A., Bengs, V., &; Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. ArXiv:2002.04275.

Tornede, A., Wever, M. D., Werner, S., Mohr, F., &; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.

Richter, C., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.

Wever, M. D., van Rooijen, L., &; Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193.https://doi.org/10.1162/evco_a_00266 

Pfannschmidt, K., Gupta, P., &; Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.

Merten, M.-L., Seemann, N., &; Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch, 142, 124–146.

Bengs, V., &; Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. ArXiv:1903.09864.

Wever, M. D., Mohr, F., Hüllermeier, E., &; Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.

Tagne, V. K., Fotso, S., Fono, L. A., &; Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213.

Couso, I., Borgelt, C., Hüllermeier, E., &; Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44.https://doi.org/10.1109/mci.2018.2881642 

Waegeman, W., Dembczynski, K., &; Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324.https://doi.org/10.1007/s10618-018-0595-5 

Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., &; Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.

Ahmadi Fahandar, M., &; Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham.https://doi.org/10.1007/978-3-030-33778-0_22 

Ahmadi Fahandar, M., &; Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham.https://doi.org/10.1007/978-3-030-30179-8_3 

Nguyen, V.-L., Destercke, S., &; Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham.https://doi.org/10.1007/978-3-030-33778-0_7 

Melnikov, V., &; Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101).https://doi.org/10.1016/j.jmva.2019.02.017 

Epple, N., Dari, S., Drees, L., Protschky, V., &; Riener, A. (2019). Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV).https://doi.org/10.1109/ivs.2019.8814100 

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, &; R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.

Brinker, K., &; Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.

Hüllermeier, E., Couso, I., &; Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.

Henzgen, S., &; Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36.https://doi.org/10.1145/3363572 

Bengs, V., Eulert, M., &; Holzmann, H. (2019). Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis, 291–312.https://doi.org/10.1016/j.jmva.2019.02.017 

Bengs, V., &; Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579.https://doi.org/10.1214/19-ejs1555 

Mohr, F., Wever, M. D., Tornede, A., &; Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.

Wever, M. D., Mohr, F., Tornede, A., &; Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.

Rohlfing, K., Leonardi, G., Nomikou, I., Rczaszek-Leonardi, J., &; Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems.https://doi.org/10.1109/TCDS.2019.2892991 

Mohr, F., Wever, M. D., Hüllermeier, E., &; Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE.https://doi.org/10.1109/SCC.2018.00039 

Pfannschmidt, K., Gupta, P., &; Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.

Mohr, F., Lettmann, T., Hüllermeier, E., &; Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.

Ramaswamy, A., &; Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control, 64(6), 2614–2620.

Demirel, B., Ramaswamy, A., Quevedo, D. E., &; Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737–742.

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society.https://doi.org/10.1109/SCC.2018.00036 

Melnikov, V., &; Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning.https://doi.org/10.1007/s10994-018-5733-1 

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515.https://doi.org/10.1007/s10994-018-5735-z 

Mohr, F., Wever, M. D., &; Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands.https://doi.org/10.1007/978-3-030-01768-2_19 

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM.https://doi.org/10.1145/3205455.3205562 

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). Automated machine learning service composition.

Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität Paderborn.

Scheibl, M. (2018). Learning about learning curves from dataset properties. Universität Paderborn.

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-030-01771-2_11 

Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C.,  Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).

Couso, I., &; Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, &; C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.

Schäfer, D., &; Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.

Ahmadi Fahandar, M., &; Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).

El Mesaoudi-Paul, A., Hüllermeier, E., &; Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.

Hesse, M., Timmermann, J., Hüllermeier, E., &; Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20.

Mencia, E. L., Fürnkranz, J., Hüllermeier, E., &; Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, &; M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.

Nguyen, V.-L., Destercke, S., Masson, M.-H., &; Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095.

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175.

Schäfer, D., &; Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.

Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., &; Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart &; D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft.

Ramaswamy, A., &; Bhatnagar, S. (2017). Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control, 63(5), 1465–1471.

Ramaswamy, A., &; Bhatnagar, S. (2017). A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions. Mathematics of Operations Research, 42(3), 648–661.

Melnikov, V., &; Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing.https://doi.org/10.5445/KSP/1000074341 

Jakobs, M.-C., Krämer, J., van Straaten, D., &; Lettmann, T. (2017). Certiû01cation Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 7–12).

Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., &; Hüllermeier, E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL).https://doi.org/10.18653/v1/W17-2206 

Schnitker, N. N. (2017). Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn.

Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink, S.,  Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.

Hetzer, A., &; Tornede, T. (2017). Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn.

Czech, M., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26).https://doi.org/10.1145/3121257.3121262 

Czech, M., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.

Fürnkranz, J., &; Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).

Fürnkranz, J., &; Hüllermeier, E. (2017). Preference Learning. In C. Sammut &; G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.

Melnikov, V., &; Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, &; R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing.

Czech, M., Hüllermeier, E., Jacobs, M. C., &; Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany.

Couso, I., Dubois, D., &; Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer.

Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T.,  Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).

Ahmadi Fahandar, M., Hüllermeier, E., &; Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).

Mohr, F., Lettmann, T., &; Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206).https://doi.org/10.1007/978-3-319-67190-1_15 

Czech, M., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).

Couso, I., Dubois, D., &; Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16).

Ahmadi Fahandar, M., &; Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.

Hoffmann, F., Hüllermeier, E., &; Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.