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ORCIDIsabelle Kuhlmann
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2020 – today
- 2024
[j1]Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant:
Paraconsistent reasoning for inconsistency measurement in declarative process specifications. Inf. Syst. 122: 102347 (2024)- [c16]Jonas Klein
, Isabelle Kuhlmann, Matthias Thimm:
Cluster-Specific Rule Mining for Argumentation-Based Classification. RATIO 2024: 57-67 - [c15]Sandra Hoffmann, Isabelle Kuhlmann, Matthias Thimm:
Enhancing Abstract Argumentation Solvers with Machine Learning-Guided Heuristics: A Feasibility Study. RATIO 2024: 185-201 - [c14]Isabelle Kuhlmann, Carl Corea:
Inconsistency Measurement in LTL f Based on Minimal Inconsistent Sets and Minimal Correction Sets. SUM 2024: 217-232 - [i3]Lars Bengel, Lydia Blümel, Elfia Bezou-Vrakatseli, Federico Castagna, Giulia D'Agostino, Isabelle Kuhlmann, Jack Mumford, Daphne Odekerken, Fabrizio Russo, Stefan Sarkadi, Madeleine Waller, Andreas Xydis:
Online Handbook of Argumentation for AI: Volume 4. CoRR abs/2401.09444 (2024) - 2023
- [c13]Isabelle Kuhlmann, Carl Corea, John Grant:
Non-Automata Based Conformance Checking of Declarative Process Specifications Based on ASP. Business Process Management Workshops 2023: 396-408 - [c12]Andreas Niskanen, Isabelle Kuhlmann, Matthias Thimm, Matti Järvisalo:
MaxSAT-Based Inconsistency Measurement. ECAI 2023: 1779-1786 - [c11]Isabelle Kuhlmann, Andreas Niskanen, Matti Järvisalo:
Computing MUS-Based Inconsistency Measures. JELIA 2023: 745-755 - [c10]Isabelle Kuhlmann, Matthias Thimm:
A Discussion of Challenges in Benchmark Generation for Abstract Argumentation. Arg&App@KR 2023: 78-84 - [c9]Isabelle Kuhlmann, Carl Corea, John Grant:
An ASP-Based Framework for Solving Problems Related to Declarative Process Specifications. NMR 2023: 129-132 - [i2]Isabelle Kuhlmann, Anna Gessler, Vivien Laszlo, Matthias Thimm:
Comparison of SAT-based and ASP-based Algorithms for Inconsistency Measurement. CoRR abs/2304.14832 (2023) - 2022
- [c8]Jonas Klein, Isabelle Kuhlmann, Matthias Thimm:
Graph Neural Networks for Algorithm Selection in Abstract Argumentation. ArgML@COMMA 2022: 81-95 - [c7]Isabelle Kuhlmann, Thorsten Wujek, Matthias Thimm:
On the Impact of Data Selection when Applying Machine Learning in Abstract Argumentation. COMMA 2022: 224-235 - [c6]Isabelle Kuhlmann, Anna Gessler, Vivien Laszlo, Matthias Thimm:
A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure. SUM 2022: 139-153 - [e1]Isabelle Kuhlmann, Jack Mumford, Stefan Sarkadi:
Proceedings of the 1st Workshop on Argumentation & Machine Learning co-located with 9th International Conference on Computational Models of Argument (COMMA 2022), Cardiff, Wales, September 13th, 2022. CEUR Workshop Proceedings 3208, CEUR-WS.org 2022 [contents] - 2021
- [c5]Isabelle Kuhlmann, Viktor Seib, Dietrich Paulus:
Integrating Feedforward Design into a Generative Network to Synthesize Supplementary Training Data for Object Classification. ICARSC 2021: 215-220 - [c4]Isabelle Kuhlmann, Tjitze Rienstra, Lars Bengel, Kenneth Skiba, Matthias Thimm:
Distinguishability in Abstract Argumentation. KR 2021: 686-690 - 2020
- [c3]Isabelle Kuhlmann, Matthias Thimm:
An Algorithm for the Contension Inconsistency Measure Using Reductions to Answer Set Programming. SUM 2020: 289-296
2010 – 2019
- 2019
- [c2]Raphael Memmesheimer, Isabelle Kuhlmann, Mark Mints, Patrik Schmidt, Christian Korbach, Ida Germann, Dietrich Paulus:
Scratchy: A Lightweight Modular Autonomous Robot for Robotic Competitions. ICARSC 2019: 1-6 - [c1]Isabelle Kuhlmann, Matthias Thimm:
Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study. SUM 2019: 24-37 - [i1]Raphael Memmesheimer, Isabelle Kuhlmann, Mark Mints, Patrik Schmidt, Christian Korbach, Ida Germann, Dietrich Paulus:
Scratchy: A Lightweight Modular Autonomous Robot for Robotic Competitions. CoRR abs/1905.05642 (2019)
Coauthor Index
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