| Using graph convolutional networks for approximate reasoning with abstract argumentation frameworks: A feasibility study I Kuhlmann, M Thimm International Conference on Scalable Uncertainty Management, 24-37, 2019 | 41 | 2019 |
| On the Impact of Data Selection when Applying Machine Learning in Abstract Argumentation I Kuhlmann, T Wujek, M Thimm Computational Models of Argument, 224-235, 2022 | 10 | 2022 |
| A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure I Kuhlmann, A Gessler, V Laszlo, M Thimm International Conference on Scalable Uncertainty Management, 139-153, 2022 | 9 | 2022 |
| An algorithm for the contension inconsistency measure using reductions to answer set programming I Kuhlmann, M Thimm Scalable Uncertainty Management: 14th International Conference, SUM 2020 …, 2020 | 9 | 2020 |
| Graph Neural Networks for Algorithm Selection in Abstract Argumentation. J Klein, I Kuhlmann, M Thimm ArgML@ COMMA, 81-95, 2022 | 7 | 2022 |
| Algorithms for inconsistency measurement using answer set programming I Kuhlmann, M Thimm 19th International Workshop on Non-Monotonic Reasoning (NMR), 159-168, 2021 | 7 | 2021 |
| Comparison of SAT-based and ASP-based Algorithms for Inconsistency Measurement I Kuhlmann, A Gessler, V Laszlo, M Thimm arXiv preprint arXiv:2304.14832, 2023 | 4 | 2023 |
| Towards Eliciting Attacks in Abstract Argumentation Frameworks I Kuhlmann Online Handbook of Argumentation for AI 2, 2021 | 3 | 2021 |
| Paraconsistent reasoning for inconsistency measurement in declarative process specifications C Corea, I Kuhlmann, M Thimm, J Grant Information Systems 122, 102347, 2024 | 2 | 2024 |
| MaxSAT-Based Inconsistency Measurement A Niskanen, I Kuhlmann, M Thimm, M Järvisalo Proceedings of the 26th European Conference on Artificial Intelligence (ECAI …, 2023 | 2 | 2023 |
| Distinguishability in Abstract Argumentation I Kuhlmann, T Rienstra, L Bengel, K Skiba, M Thimm Proceedings of the International Conference on Principles of Knowledge …, 2021 | 2 | 2021 |
| Computing MUS-based inconsistency measures I Kuhlmann, A Niskanen, M Järvisalo European Conference on Logics in Artificial Intelligence, 745-755, 2023 | 1 | 2023 |
| Non-Automata Based Conformance Checking of Declarative Process Specifications Based on ASP I Kuhlmann, C Corea, J Grant International Conference on Business Process Management, 396-408, 2023 | 1 | 2023 |
| An ASP-Based Framework for Solving Problems Related to Declarative Process Specifications. I Kuhlmann, C Corea, J Grant NMR, 129-132, 2023 | 1 | 2023 |
| Online Handbook of Argumentation for AI: Volume 2 A Brannstrom, F Castagna, T Duchatelle, M Foulis, T Kampik, I Kuhlmann, ... arXiv preprint arXiv:2106.10832, 2021 | 1 | 2021 |
| Integrating Feedforward Design into a Generative Network to Synthesize Supplementary Training Data for Object Classification I Kuhlmann, V Seib, D Paulus 2021 IEEE International Conference on Autonomous Robot Systems and …, 2021 | 1 | 2021 |
| Scratchy: a lightweight modular autonomous robot for robotic competitions R Memmesheimer, I Kuhlmann, M Mints, P Schmidt, C Korbach, ... 2019 IEEE International Conference on Autonomous Robot Systems and …, 2019 | 1 | 2019 |
| Increasing Interpretability in Outside Knowledge Visual Question Answering M Upravitelev, C Krauss, I Kuhlmann International Conference on Knowledge Management in Organizations, 319-330, 2024 | | 2024 |
| Enhancing Abstract Argumentation Solvers with Machine Learning-Guided Heuristics: A Feasibility Study S Hoffmann, I Kuhlmann, M Thimm Conference on Advances in Robust Argumentation Machines, 185-201, 2024 | | 2024 |
| Cluster-Specific Rule Mining for Argumentation-Based Classification J Klein, I Kuhlmann, M Thimm Conference on Advances in Robust Argumentation Machines, 57-67, 2024 | | 2024 |
Matthias ThimmUniversity of HagenVerified email at fernuni-hagen.de
