Isabelle Kuhlmann

Selected Publications

• Isabelle Kuhlmann, Carl Corea. Inconsistency Measurement in LTLf Based on Minimal Inconsistent Sets and Minimal Correction Sets. In Proceedings of the 16th International Conference on Scalable Uncertainty Management (SUM'24). November 2024.
• Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant. Paraconsistent Reasoning for Inconsistency Measurement in Declarative Process Specifications. In Information Systems, 122:102347. February 2024.
• Isabelle Kuhlmann, Andreas Niskanen, Matti Järvisalo. Computing MUS-Based Inconsistency Measures. In Proceedings of the European Conference on Logics in Artificial Intelligence (JELIA'23). September 2023.
• Andreas Niskanen, Isabelle Kuhlmann, Matthias Thimm, Matti Järvisalo. MaxSAT-Based Inconsistency Measurement. In Proceedings of the 26th European Conference on Artificial Intelligence (ECAI'23). September 2023.
• Isabelle Kuhlmann, Carl Corea, John Grant. An ASP-Based Framework for Solving Problems Related to Declarative Process Specifications. In Proceedings of the 21st International Workshop on Non-Monotonic Reasoning (NMR'23). September 2023.
• Isabelle Kuhlmann, Thorsten Wujek, Matthias Thimm. On the Impact of Data Selection when Applying Machine Learning in Abstract Argumentation. In Proceedings of the 9th International Conference on Computational Models of Argument (COMMA'22). September 2022.
• Isabelle Kuhlmann, Tjitze Rienstra, Lars Bengel, Kenneth Skiba and Matthias Thimm. Distinguishability inAbstract Argumentation. Proceedings of the 18th International Conference on Principles ofKnowledge Representation and Reasoning (KR’21), 2021.
• Isabelle Kuhlmann, Viktor Seib and Dietrich Paulus. Integrating Feedforward Design into a GenerativeNetwork to Synthesize Supplementary Training Data for Object Classification. In Proceedings of the 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC'21). April 2021.
• Isabelle Kuhlmann, Matthias Thimm. An Algorithm for the Contension Inconsistency Measure using Reductions to Answer Set Programming. In Proceedings of the 14th International Conference on Scalable Uncertainty Management (SUM'20). September 2020. (Best Student Paper)
• Isabelle Kuhlmann, Matthias Thimm. Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study. In Proceedings of the 13th International Conference on Scalable Uncertainty Management (SUM'19). December 2019.