@inbook{61820,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>A scoring list is a sequence of simple decision models, where features are incrementally evaluated and scores of satisfied features are summed to be used for threshold-based decisions or for calculating class probabilities. In this paper, we introduce a new multi-class variant and compare it against previously introduced binary classification variants for incremental decisions, as well as multi-class variants for classical decision-making using all features. Furthermore, we introduce a new multi-class dataset to assess collaborative human-machine decision-making, which is suitable for user studies with non-expert participants. We demonstrate the usefulness of our approach by evaluating predictive performance and compared to the performance of participants without AI help.</jats:p>}},
  author       = {{Heid, Stefan and Kornowicz, Jaroslaw and Hanselle, Jonas and Thommes, Kirsten and Hüllermeier, Eyke}},
  booktitle    = {{Communications in Computer and Information Science}},
  isbn         = {{9783032083265}},
  issn         = {{1865-0929}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making}}},
  doi          = {{10.1007/978-3-032-08327-2_6}},
  year         = {{2025}},
}

@article{54911,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@article{54910,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@article{54907,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@inproceedings{57645,
  author       = {{Heid, Stefan and Kornowicz, Jaroslaw and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Thommes, Kirsten}},
  booktitle    = {{PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE}},
  pages        = {{233}},
  title        = {{{Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems}}},
  volume       = {{21}},
  year         = {{2024}},
}

@inproceedings{51209,
  author       = {{Hanselle, Jonas Manuel and Kornowicz, Jaroslaw and Heid, Stefan and Thommes, Kirsten and Hüllermeier, Eyke}},
  booktitle    = {{LWDA’23: Learning, Knowledge, Data, Analysis. }},
  editor       = {{Leyer, M and Wichmann, J}},
  issn         = {{1613-0073}},
  title        = {{{Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain}}},
  year         = {{2023}},
}

@article{29653,
  author       = {{Weber, Daniel and Heid, Stefan and Bode, Henrik and Lange, Jarren and Hüllermeier, Eyke and Wallscheid, Oliver}},
  journal      = {{IEEE Access}},
  pages        = {{35654–35669}},
  publisher    = {{IEEE}},
  title        = {{{Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments}}},
  doi          = {{10.1109/ACCESS.2021.3062144}},
  volume       = {{9}},
  year         = {{2021}},
}

@unpublished{19603,
  abstract     = {{Micro- and smart grids (MSG) play an important role both for integrating
renewable energy sources in conventional electricity grids and for providing
power supply in remote areas. Modern MSGs are largely driven by power
electronic converters due to their high efficiency and flexibility.
Nevertheless, controlling MSGs is a challenging task due to highest
requirements on energy availability, safety and voltage quality within a wide
range of different MSG topologies. This results in a high demand for
comprehensive testing of new control concepts during their development phase
and comparisons with the state of the art in order to ensure their feasibility.
This applies in particular to data-driven control approaches from the field of
reinforcement learning (RL), whose stability and operating behavior can hardly
be evaluated a priori. Therefore, the OpenModelica Microgrid Gym (OMG) package,
an open-source software toolbox for the simulation and control optimization of
MSGs, is proposed. It is capable of modeling and simulating arbitrary MSG
topologies and offers a Python-based interface for plug \& play controller
testing. In particular, the standardized OpenAI Gym interface allows for easy
RL-based controller integration. Besides the presentation of the OMG toolbox,
application examples are highlighted including safe Bayesian optimization for
low-level controller tuning.}},
  author       = {{Bode, Henrik and Heid, Stefan Helmut and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver}},
  booktitle    = {{arXiv:2005.04869}},
  title        = {{{Towards a Scalable and Flexible Simulation and Testing Environment  Toolbox for Intelligent Microgrid Control}}},
  year         = {{2020}},
}

@inproceedings{24146,
  author       = {{Heid, Stefan Helmut and Ramaswamy, Arunselvan and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020}},
  pages        = {{247}},
  title        = {{{Constrained Multi-Agent Optimization with Unbounded Information Delay}}},
  volume       = {{26}},
  year         = {{2020}},
}

@unpublished{17605,
  abstract     = {{Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. 
While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.
These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead  of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.
In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.}},
  author       = {{Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  booktitle    = {{Journal of Data Mining and Digital Humanities}},
  publisher    = {{episciences}},
  title        = {{{Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}}},
  year         = {{2020}},
}

@article{29649,
  author       = {{Heid, Stefan and Weber, Daniel and Bode, Henrik and Hüllermeier, Eyke and Wallscheid, Oliver}},
  journal      = {{Journal of Open Source Software}},
  number       = {{54}},
  pages        = {{2435}},
  title        = {{{OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control}}},
  volume       = {{5}},
  year         = {{2020}},
}

@article{29644,
  author       = {{Bode, Henrik and Heid, Stefan and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver}},
  journal      = {{arXiv preprint arXiv:2005.04869}},
  title        = {{{Towards a scalable and flexible simulation and testing environment toolbox for intelligent microgrid control}}},
  year         = {{2020}},
}