Scratchy: A Lightweight Modular Autonomous Robot for Robotic Competitions. - Raphael Memmesheimer; Isabelle Kuhlmann; Mark Mints; Patrik Schmidt; Christian Korbach; Ida Germann; Dietrich Paulus (ICARSC, 2019)

Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study. - Isabelle Kuhlmann; Matthias Thimm (SUM, 2019)

An Algorithm for the Contension Inconsistency Measure Using Reductions to Answer Set Programming. - Isabelle Kuhlmann; Matthias Thimm (SUM, 2020)

Integrating Feedforward Design into a Generative Network to Synthesize Supplementary Training Data for Object Classification. - Isabelle Kuhlmann; Viktor Seib; Dietrich Paulus (ICARSC, 2021)

Distinguishability in Abstract Argumentation. - Isabelle Kuhlmann; Tjitze Rienstra; Lars Bengel; Kenneth Skiba; Matthias Thimm (KR, 2021)

Graph Neural Networks for Algorithm Selection in Abstract Argumentation. - Jonas Klein; Isabelle Kuhlmann; Matthias Thimm (ArgML@COMMA, 2022)

On the Impact of Data Selection when Applying Machine Learning in Abstract Argumentation. - Isabelle Kuhlmann; Thorsten Wujek; Matthias Thimm (COMMA, 2022)

A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure. - Isabelle Kuhlmann; Anna Gessler; Vivien Laszlo; Matthias Thimm (SUM, 2022)

Non-Automata Based Conformance Checking of Declarative Process Specifications Based on ASP. - Isabelle Kuhlmann; Carl Corea; John Grant (Business Process Management Workshops, 2023)

MaxSAT-Based Inconsistency Measurement. - Andreas Niskanen; Isabelle Kuhlmann; Matthias Thimm; Matti Järvisalo (ECAI, 2023)

Computing MUS-Based Inconsistency Measures. - Isabelle Kuhlmann; Andreas Niskanen; Matti Järvisalo (JELIA, 2023)

A Discussion of Challenges in Benchmark Generation for Abstract Argumentation. - Isabelle Kuhlmann; Matthias Thimm (Arg&App@KR, 2023)

An ASP-Based Framework for Solving Problems Related to Declarative Process Specifications. - Isabelle Kuhlmann; Carl Corea; John Grant (NMR, 2023)

Enhancing Abstract Argumentation Solvers with Machine Learning-Guided Heuristics: A Feasibility Study. - Sandra Hoffmann; Isabelle Kuhlmann; Matthias Thimm (RATIO, 2024)

Cluster-Specific Rule Mining for Argumentation-Based Classification. - Jonas Klein; Isabelle Kuhlmann; Matthias Thimm (RATIO, 2024)

Inconsistency Measurement in LTL f Based on Minimal Inconsistent Sets and Minimal Correction Sets. - Isabelle Kuhlmann; Carl Corea (SUM, 2024)

Online Handbook of Argumentation for AI: Volume 4. - Lars Bengel; Lydia Blümel; Elfia Bezou-Vrakatseli; Federico Castagna; Giulia D'Agostino; Isabelle Kuhlmann; Jack Mumford; Daphne Odekerken; Fabrizio Russo 002; Stefan Sarkadi; Madeleine Waller; Andreas Xydis (CoRR, 2024)

Paraconsistent reasoning for inconsistency measurement in declarative process specifications. - Carl Corea; Isabelle Kuhlmann; Matthias Thimm; John Grant (Inf. Syst., 2024)

Comparison of SAT-Based and ASP-Based Algorithms for Inconsistency Measurement. - Isabelle Kuhlmann; Anna Gessler; Vivien Laszlo; Matthias Thimm (J. Artif. Intell. Res., 2025)