Intern
KI 2024

Accepted Papers

Accepted Full Paper

  • Nils Kemmerzell and Annika Schreiner - Quantifying the Trade-Offs between Dimensions of Trustworthy AI - An Empirical Study on Fairness, Explainability, Privacy, and Robustness
  • Julian Tritscher, Maximilian Wolf, Anna Krause, Andreas Hotho and Daniel Schlör - Data Generation for Explainable Occupational Fraud Detection
  • Wilson Sentanoe, Sreyashi Saha, Yasas Diyanananda, Aqsa Manzoor, Buddhika Dasanayake and Daniela Thyssens - Graph2RETA: Graph Neural Networks for Pick-up and Delivery Route Prediction and Arrival Time Estimation
  • Julia Ruttmann and Alexander Schiendorfer - SocialCOP: Reusable Building Blocks for Collective Constraint Optimization
  • Claudia Schon and Oliver Jakobs - Context-Specific Selection of Commonsense Knowledge Using Large Language Models
  • Hannes Kath, Thiago S. Gouvêa and Daniel Sonntag - Active Learning in Multi-label Classification of Bioacoustic Data
  • Jan Roßbach, Ahmed Hammam, Oliver De Candido and Michael Leuschel - Evaluating AI-based Components for Autonomous Railway Systems
  • Ilira Troshani, Thiago Gouvêa and Daniel Sonntag - Leveraging Weakly Supervised and Multiple Instance Learning for Multi-label Classification of Passive Acoustic Monitoring Data
  • Timo Bertram, Johannes Fürnkranz and Martin Müller - Efficiently Training Neural Networks for Imperfect-Information Games by Sampling Information Sets
  • Malte Luttermann, Ralf Möller and Mattis Hartwig - Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models
  • Christopher Bonenberger, Markus Schneider, Wolfgang Ertel and Friedhelm Schwenker - A Note on Linear Time Series Prediction
  • Justus Fries, Michael Freund and Andreas Harth - SaVeWoT: Scripting and Verifying Web of Things Systems and Their Effects on the Physical World
  • Sami Kharma and Jürgen Großmann - Dataset Quality Assessment Through Descriptive Out-of-Distribution Detection
  • Anna Kruspe and Mila Stillman - Saxony-Anhalt is the worst: Bias towards German federal states in Large Language Models
  • Xuzhe Dang and Stefan Edelkamp - Data Augmentation in Latent Space with Variational Autoencoder and Pretrained Image Model for Visual Reinforcement Learning
  • Moritz Bayerkuhnlein, Tobias Schwartz and Diedrich Wolter - From Resolving Inconsistencies in Qualitative Constraints Networks to Identifying Robust Solutions: A Universal Encoding in ASP
  • Björn Filter, Ralf Möller and Özgür Özçep - Mechanisms for Data Sharing in Collaborative Causal Inference
  • Stefan Edelkamp - Could the Declarer have Discarded It? Refined Anticipation of Cards in Skat
  • Stefan Edelkamp - A Framework for General Trick-Taking Card Games

Accepted Short Paper

  • Quirin Göttl, Haris Asif, Alexander Mattick, Robert Marzilger and Axel Plinge - Automated Design in Hybrid Action Spaces by Reinforcement Learning and Differential Evolution
  • Aray Karjauv and Sahin Albayrak - LaFAM: Unsupervised Feature Attribution with Label-free Activation Maps
  • Emanuel Slany, Stephan Scheele and Ute Schmid - Explanatory Interactive Machine Learning with Counterexamples from Constrained Large Language Models
  • Christian Beecks, Anandraj Amalraj, Alexander Graß, Marc Jentsch, Felix Kitschke, Maximilian Norz and Patric Schäffer - Leveraging YOLO for Real-Time Video Analysis of Animal Welfare in Pig Slaughtering Processes
  • Anna Riedmann, Julia Götz, Carlo D’Eramo and Birgit Lugrin - Uli-RL: A Real-World Deep Reinforcement Learning Pedagogical Agent for Children
  • Vanessa Borst, Timo Dittus, Konstantin Müller and Samuel Kounev - Early Explorations of Lightweight Neural Networks for Wound Segmentation on Mobile Devices
  • Julian Haasis, Christopher Bonenberger and Markus Schneider - Instance segmentation with a novel tree log detection dataset