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Engelhardt, Raphael; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Exploring the Reliability of SHAP Values in Reinforcement Learning Proceedings Article
In: Longo, Luca; Lapuschkin, Sebastian; Seifert, Christin (Hrsg.): Explainable Artificial Intelligence: Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III, S. 165–184, Springer, Cham, 2024, ISBN: 978-3-031-63799-5.
@inproceedings{Engelhardt2024b,
title = {Exploring the Reliability of SHAP Values in Reinforcement Learning},
author = {Raphael Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Luca Longo and Sebastian Lapuschkin and Christin Seifert},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Engelh24a.d/Evaluation_of_SHAP_for_RL_XAI2024.pdf},
doi = {10.1007/978-3-031-63800-8},
isbn = {978-3-031-63799-5},
year = {2024},
date = {2024-07-09},
booktitle = {Explainable Artificial Intelligence: Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III},
volume = {2155},
pages = {165–184},
publisher = {Springer},
address = {Cham},
series = {Communications in Computer and Information Science},
keywords = {Explainability; Reinforcement Learning; SHAP; Shapley Values; XAI},
pubstate = {published},
tppubtype = {inproceedings}
}
Engelhardt, Raphael C; Meinen, Marcel J; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task Artikel
In: arXiv preprint arXiv:2412.04974, 2024.
@article{Engelhardt2024,
title = {Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task},
author = {Raphael C Engelhardt and Marcel J Meinen and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
url = {https://arxiv.org/pdf/2412.04974},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {arXiv preprint arXiv:2412.04974},
keywords = {AI, decision trees, deep learning, explainable AI, Reinforcement learning, RL3},
pubstate = {published},
tppubtype = {article}
}
Lange, Moritz; Engelhardt, Raphael C; Konen, Wolfgang; Wiskott, Laurenz
Beyond Trial and Error in Reinforcement Learning Proceedings Article
In: Proceedings of the DataNinja sAIOnARA 2024 Conference, S. 58, 2024.
@inproceedings{Lange2024,
title = {Beyond Trial and Error in Reinforcement Learning},
author = {Moritz Lange and Raphael C Engelhardt and Wolfgang Konen and Laurenz Wiskott},
url = {https://biecoll.ub.uni-bielefeld.de/index.php/dataninja/issue/download/82/1#page=63},
doi = {10.11576/dataninja-1156},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the DataNinja sAIOnARA 2024 Conference},
pages = {58},
keywords = {Reinforcement learning, Representation learning, RL3},
pubstate = {published},
tppubtype = {inproceedings}
}
Lange, Moritz; Engelhardt, Raphael C; Konen, Wolfgang; Wiskott, Laurenz
Interpretable brain-inspired representations improve RL performance on visual navigation tasks Artikel
In: arXiv preprint arXiv:2402.12067, 2024.
@article{Lange2024a,
title = {Interpretable brain-inspired representations improve RL performance on visual navigation tasks},
author = {Moritz Lange and Raphael C Engelhardt and Wolfgang Konen and Laurenz Wiskott},
url = {https://arxiv.org/pdf/2402.12067},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {arXiv preprint arXiv:2402.12067},
keywords = {Reinforcement learning, Representation learning, RL3},
pubstate = {published},
tppubtype = {article}
}
Engelhardt, Raphael C.; Raycheva, Ralitsa; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers, Springer Nature Switzerland, Imprint: Springer, 2024.
@inproceedings{Engelhardt2024a,
title = {Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning},
author = {Raphael C. Engelhardt and Ralitsa Raycheva and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Engelh23a.d/Engelh23a.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers},
publisher = {Springer Nature Switzerland, Imprint: Springer},
keywords = {Cybernetics, Deep Reinforcement Learning, Large Action Space, RL3, Serious Games},
pubstate = {published},
tppubtype = {inproceedings}
}
Lange, Moritz; Krystiniak, Noah; Engelhardt, Raphael C.; Konen, Wolfgang; Wiskott, Laurenz
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers, Springer Nature Switzerland, Imprint: Springer, 2024, (Best Paper Award).
@inproceedings{Lange2024b,
title = {Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison},
author = {Moritz Lange and Noah Krystiniak and Raphael C. Engelhardt and Wolfgang Konen and Laurenz Wiskott},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
url = {https://link.springer.com/book/10.1007/978-3-031-53969-5},
year = {2024},
date = {2024-01-01},
booktitle = {Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers},
publisher = {Springer Nature Switzerland, Imprint: Springer},
note = {Best Paper Award},
keywords = {auxiliary tasks, Reinforcement learning, Representation learning, RL3},
pubstate = {published},
tppubtype = {inproceedings}
}
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: arXiv preprint arXiv:2405.15512, 2024.
@article{Oedingen2024,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://arxiv.org/abs/2405.15512},
year = {2024},
date = {2024-01-01},
journal = {arXiv preprint arXiv:2405.15512},
keywords = {AI, ChatGPT, Code Detection, Large Language Models, machine learning},
pubstate = {published},
tppubtype = {article}
}
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: AI, Bd. 5, Nr. 3, S. 1066–1094, 2024, ISSN: 2673-2688.
@article{Oedingen2024a,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://www.mdpi.com/2673-2688/5/3/53},
doi = {10.3390/ai5030053},
issn = {2673-2688},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {AI},
volume = {5},
number = {3},
pages = {1066–1094},
abstract = {In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.},
keywords = {AI, ChatGPT, Code Detection, Large Language Models, machine learning},
pubstate = {published},
tppubtype = {article}
}
In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.
2023
Lange, Moritz; Krystiniak, Noah; Engelhardt, Raphael C.; Konen, Wolfgang; Wiskott, Laurenz
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): 9th International Conference on machine Learning, Optimization, and Data Science (LOD2023), 2023.
@inproceedings{Lange2023,
title = {Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison},
author = {Moritz Lange and Noah Krystiniak and Raphael C. Engelhardt and Wolfgang Konen and Laurenz Wiskott},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
year = {2023},
date = {2023-01-01},
booktitle = {9th International Conference on machine Learning, Optimization, and Data Science (LOD2023)},
keywords = {auxiliary tasks, Reinforcement learning, Representation learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Konen, Wolfgang
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning Artikel
In: arXiv preprint arXiv:2301.12167, 2023.
@article{Konen2023,
title = {Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning},
author = {Wolfgang Konen},
url = {https://arxiv.org/abs/2301.12167},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {arXiv preprint arXiv:2301.12167},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
Engelhardt, Raphael C; Oedingen, Marc; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Iterative Oblique Decision Trees Deliver Explainable RL Models Artikel
In: Algorithms, Bd. 16, Nr. 6, S. 282, 2023.
@article{Engelhardt2023,
title = {Iterative Oblique Decision Trees Deliver Explainable RL Models},
author = {Raphael C Engelhardt and Marc Oedingen and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
url = {https://www.mdpi.com/1999-4893/16/6/282},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Algorithms},
volume = {16},
number = {6},
pages = {282},
publisher = {MDPI},
keywords = {AI, decision trees, deep learning, explainable AI, Reinforcement learning, RL3},
pubstate = {published},
tppubtype = {article}
}
Engelhardt, Raphael C.; Raycheva, Ralitsa; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): 9th International Conference on machine Learning, Optimization, and Data Science (LOD2023), 2023.
@inproceedings{Engelhardt2023a,
title = {Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning},
author = {Raphael C. Engelhardt and Ralitsa Raycheva and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Engelh23a.d/Engelh23a.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {9th International Conference on machine Learning, Optimization, and Data Science (LOD2023)},
keywords = {Cybernetics, Deep Reinforcement Learning, Game Learning, Large Action Space, Serious Games},
pubstate = {published},
tppubtype = {inproceedings}
}
Seven, Meltem
KI-Agenten im Vergleich: Erfolg von Agenten des Ludii General Game Systems in Partien gegen perfekte oder starke Agenten am Beispiel der Spiele Vier Gewinnt, Nim und Othello Abschlussarbeit
TH Köln – University of Applied Sciences, 2023, (Bachelor thesis).
@mastersthesis{Seven2023,
title = {KI-Agenten im Vergleich: Erfolg von Agenten des Ludii General Game Systems in Partien gegen perfekte oder starke Agenten am Beispiel der Spiele Vier Gewinnt, Nim und Othello},
author = {Meltem Seven},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Meltem-Seven-final.pdf},
year = {2023},
date = {2023-01-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {BT-MT, Game Learning, GBG, Ludii},
pubstate = {published},
tppubtype = {mastersthesis}
}
2022
Konen, Wolfgang
The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning Forschungsbericht
TH Köln 2022.
@techreport{Konen2022,
title = {The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning},
author = {Wolfgang Konen},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Konen22a.d/TR-GBG.pdf},
year = {2022},
date = {2022-09-01},
institution = {TH Köln},
school = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Weitz, Ann
Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath Abschlussarbeit
TH Köln – University of Applied Sciences, 2022, (Bachelor thesis).
@mastersthesis{Weitz2022b,
title = {Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath},
author = {Ann Weitz},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Weitz-final-2022.pdf},
year = {2022},
date = {2022-05-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Weitz, Ann
Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework Forschungsbericht
2022, (Praxisprojekt).
@techreport{Weitz2022,
title = {Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework},
author = {Ann Weitz},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/PP-Doku-Weitz-2022-02.pdf},
year = {2022},
date = {2022-02-01},
school = {TH Köln – University of Applied Sciences},
note = {Praxisprojekt},
keywords = {AI, BT-MT, Game Learning, Ludii, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Cöln, Julian
KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!" Abschlussarbeit
TH Köln – University of Applied Sciences, 2022, (Bachelor thesis).
@mastersthesis{Coeln2022,
title = {KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!"},
author = {Julian Cöln},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Julian_Coeln2021-final.pdf},
year = {2022},
date = {2022-02-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, GBG, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Engelhardt, Raphael C.; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Sample-based Rule Extraction for Explainable Reinforcement Learning Proceedings Article
In: 8th International Conference on machine Learning, Optimization, and Data Science (LOD2022), 2022.
@inproceedings{Engel2022,
title = {Sample-based Rule Extraction for Explainable Reinforcement Learning},
author = {Raphael C. Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
year = {2022},
date = {2022-01-01},
booktitle = {8th International Conference on machine Learning, Optimization, and Data Science (LOD2022)},
keywords = {decision trees, deep learning, explainable AI, Reinforcement learning, RL3},
pubstate = {published},
tppubtype = {inproceedings}
}
Scheiermann, Johannes; Konen, Wolfgang
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel
In: arXiv preprint arXiv:2204.13307, 2022, (Preprint of the IEEE ToG 2022 paper).
@article{Scheier2022arXiv,
title = {AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time},
author = {Johannes Scheiermann and Wolfgang Konen},
url = {https://arxiv.org/abs/2204.13307},
year = {2022},
date = {2022-01-01},
journal = {arXiv preprint arXiv:2204.13307},
note = {Preprint of the IEEE ToG 2022 paper},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
Scheiermann, Johannes; Konen, Wolfgang
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel
In: IEEE Transactions on Games, 2022.
@article{Scheier2022,
title = {AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time},
author = {Johannes Scheiermann and Wolfgang Konen},
url = {https://ieeexplore.ieee.org/document/9893320},
doi = {10.1109/TG.2022.3206733},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Games},
keywords = {AI, deep learning, Game Learning, GBG, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
2021
Meissner, Simon
Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG) Abschlussarbeit
TH Köln – University of Applied Sciences, 2021, (Bachelor thesis).
@mastersthesis{Meissner2021,
title = {Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG)},
author = {Simon Meissner},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Meissner-final-2021.pdf},
year = {2021},
date = {2021-12-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Zeh, Tim
Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Abschlussarbeit
TH Köln – University of Applied Sciences, 2021, (Master thesis).
@mastersthesis{Zeh2021,
title = {Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework},
author = {Tim Zeh},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_Zeh_final_Poker-GBG-2021.pdf},
year = {2021},
date = {2021-07-01},
school = {TH Köln – University of Applied Sciences},
note = {Master thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Bagheri, Samineh; Reinicke, Ulf; Anders, Denis; Konen, Wolfgang
Surrogate-assisted optimization for augmentation of finite element techniques Artikel
In: Journal of Computational Science, Bd. 54, S. 101427, 2021.
@article{Bagheri2021,
title = {Surrogate-assisted optimization for augmentation of finite element techniques},
author = {Samineh Bagheri and Ulf Reinicke and Denis Anders and Wolfgang Konen},
url = {https://doi.org/10.1016/j.jocs.2021.101427},
year = {2021},
date = {2021-01-01},
journal = {Journal of Computational Science},
volume = {54},
pages = {101427},
publisher = {Elsevier},
keywords = {FEM, optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {article}
}
Engelhardt, Raphael C.; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Shedding light into the black box of reinforcement learning - extended abstract Proceedings Article
In: Hammer, Barbara; Schilling, Malte; Wiskott, Laurenz (Hrsg.): Workshop on Trustworthy AI in the Wild (at: KI 2021 - German Conf. on AI), 2021.
@inproceedings{Engel2021,
title = {Shedding light into the black box of reinforcement learning - extended abstract},
author = {Raphael C. Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Barbara Hammer and Malte Schilling and Laurenz Wiskott},
url = {https://dataninja.nrw/wp-content/uploads/2021/09/1_Engelhardt_SheddingLight_Abstract.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Workshop on Trustworthy AI in the Wild (at: KI 2021 - German Conf. on AI)},
keywords = {decision trees, deep learning, explainable AI, Reinforcement learning, RL3},
pubstate = {published},
tppubtype = {inproceedings}
}
Konen, Wolfgang; Bagheri, Samineh
Final adaptation reinforcement learning for N-player games Artikel
In: arXiv preprint arXiv:2111.14375, 2021.
@article{Konen2021,
title = {Final adaptation reinforcement learning for N-player games},
author = {Wolfgang Konen and Samineh Bagheri},
url = {https://arxiv.org/abs/2111.14375},
year = {2021},
date = {2021-01-01},
journal = {arXiv preprint arXiv:2111.14375},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
Thill, Markus; Konen, Wolfgang; Wang, Hao; Bäck, Thomas
Temporal convolutional autoencoder for unsupervised anomaly detection in time series Artikel
In: Applied Soft Computing, Bd. 112, S. 107751, 2021.
@article{Thill2021,
title = {Temporal convolutional autoencoder for unsupervised anomaly detection in time series},
author = {Markus Thill and Wolfgang Konen and Hao Wang and Thomas Bäck},
url = {https://doi.org/10.1016/j.asoc.2021.107751},
year = {2021},
date = {2021-01-01},
journal = {Applied Soft Computing},
volume = {112},
pages = {107751},
publisher = {Elsevier},
keywords = {anomaly detection, deep learning, TCN, time series},
pubstate = {published},
tppubtype = {article}
}
2020
Bagheri, Samineh
Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems Promotionsarbeit
Leiden University and TH Köln, 2020, (PhD thesis).
@phdthesis{Bagheri2020,
title = {Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems},
author = {Samineh Bagheri},
year = {2020},
date = {2020-04-01},
institution = {Institut für Informatik},
school = {Leiden University and TH Köln},
note = {PhD thesis},
keywords = {BT-MT, machine learning, MONREP, optimization, RBF, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {phdthesis}
}
Konen, Wolfgang; Bagheri, Samineh
Final Adaptation Reinforcement Learning for N-Player Games Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.
@techreport{Konen20b_TRb,
title = {Final Adaptation Reinforcement Learning for N-Player Games},
author = {Wolfgang Konen and Samineh Bagheri},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen20b_TR.d/Konen20b_TR.pdf},
year = {2020},
date = {2020-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang
The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.
@techreport{Konen20d,
title = {The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning},
author = {Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen20d.d/TR-GBG.pdf},
year = {2020},
date = {2020-01-01},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang
Die CO2-Kosten des Video-Streaming Artikel
In: Die Neue Hochschule (dnh), Bd. 3, S. 18-19, 2020.
@article{Konen20c,
title = {Die CO2-Kosten des Video-Streaming},
author = {Wolfgang Konen},
url = {https://www.th-koeln.de/mam/downloads/deutsch/hochschule/aktuell/nachrichten/dnh_2020-3_vorveroffentlichung_beitrag_konen.pdf},
year = {2020},
date = {2020-01-01},
journal = {Die Neue Hochschule (dnh)},
volume = {3},
pages = {18-19},
keywords = {digital content, online teaching, video streaming},
pubstate = {published},
tppubtype = {article}
}
Thill, Markus; Konen, Wolfgang; Bäck, Thomas
Time Series Encodings with Temporal Convolutional Networks Proceedings Article
In: Vasile, Bogdan Filipic Massimiliano (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA), 2020.
@inproceedings{Thill20a,
title = {Time Series Encodings with Temporal Convolutional Networks},
author = {Markus Thill and Wolfgang Konen and Thomas Bäck},
editor = {Bogdan Filipic Massimiliano Vasile},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Thill20a.d/bioma2020-tcn.pdf},
year = {2020},
date = {2020-01-01},
booktitle = {9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA)},
keywords = {anomaly detection, deep learning, TCN, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Scheiermann, Johannes
Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Forschungsbericht
TH Köln, Institut für Informatik 2020, (Praxisprojekt).
@techreport{Scheier2020,
title = {Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten?},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Prj-Scheiermann-2020-08.pdf},
year = {2020},
date = {2020-01-01},
institution = {TH Köln, Institut für Informatik},
note = {Praxisprojekt},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Scheiermann, Johannes
AlphaZero-inspirierte KI-Agenten im General Board Game Playing Abschlussarbeit
TH Köln -- University of Applied Sciences, 2020, (Bachelor thesis).
@mastersthesis{Scheier2020b,
title = {AlphaZero-inspirierte KI-Agenten im General Board Game Playing},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Scheiermann_final.pdf},
year = {2020},
date = {2020-01-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Konen, Wolfgang; Bagheri, Samineh
Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Proceedings Article
In: Vasile, Bogdan Filipic Massimiliano (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA), Bruxelles, 2020.
@inproceedings{Konen20bc,
title = {Reinforcement Learning for N-Player Games: The Importance of Final Adaptation},
author = {Wolfgang Konen and Samineh Bagheri},
editor = {Bogdan Filipic Massimiliano Vasile},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen20b.d/bioma20-TDNTuple.pdf},
year = {2020},
date = {2020-01-01},
booktitle = {9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA)},
address = {Bruxelles},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
Cöln, Julian; Dittmar, Yannick
Untersuchung von KI Agenten im Spiel Othello Forschungsbericht
TH Köln, Institut für Informatik 2019.
@techreport{Cöln2019,
title = {Untersuchung von KI Agenten im Spiel Othello},
author = {Julian Cöln and Yannick Dittmar},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Proj-DittmarCoeln-2019-12.pdf},
year = {2019},
date = {2019-12-01},
institution = {TH Köln, Institut für Informatik},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang
General Board Game Playing for Education and Research in Generic AI Game Learning Proceedings Article
In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): IEEE Conference on Games, London, 2019.
@inproceedings{Konen19b,
title = {General Board Game Playing for Education and Research in Generic AI Game Learning},
author = {Wolfgang Konen},
editor = {Diego Perez and Sanaz Mostaghim and Simon Lucas},
url = {https://arxiv.org/pdf/1907.06508},
year = {2019},
date = {2019-08-20},
booktitle = {IEEE Conference on Games},
address = {London},
keywords = {CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas
Solving Optimization Problems with High Conditioning by Means of Online Whitening Proceedings Article
In: Lopez-Ibanez, Manuel (Hrsg.): Genetic and Evolutionary Computation Conference 2019 (GECCO'19), Prague, S. 243-244, ACM, 2019.
@inproceedings{Bagh19c,
title = {Solving Optimization Problems with High Conditioning by Means of Online Whitening},
author = {Samineh Bagheri and Wolfgang Konen and Thomas Bäck},
editor = {Manuel Lopez-Ibanez},
url = {http://blogs.gm.fh-koeln.de/ciop/files/2019/07/GECCO2019.pdf},
year = {2019},
date = {2019-07-01},
booktitle = {Genetic and Evolutionary Computation Conference 2019 (GECCO'19), Prague},
pages = {243-244},
publisher = {ACM},
keywords = {CI, constraints, MONREP, optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {inproceedings}
}
Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning Forschungsbericht
arXiv preprint arXiv:1904.08397 2019.
@techreport{Bagh19b,
title = {SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning},
author = {Samineh Bagheri and Wolfgang Konen and Thomas Bäck},
url = {https://arxiv.org/abs/1904.08397},
year = {2019},
date = {2019-07-01},
institution = {arXiv preprint arXiv:1904.08397},
keywords = {optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {techreport}
}
Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas
How to Solve the Dilemma of Margin-Based Equality Constraint Handling Methods Artikel
In: at-Automatisierungstechnik, Bd. submitted, 2019.
@article{Bagh19a,
title = {How to Solve the Dilemma of Margin-Based Equality Constraint Handling Methods},
author = {Samineh Bagheri and Wolfgang Konen and Thomas Bäck},
year = {2019},
date = {2019-01-01},
journal = {at-Automatisierungstechnik},
volume = {submitted},
keywords = {optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {article}
}
Buhl, Henning; Konen, Wolfgang; Thill, Markus
Deep Learning mit Keras und Tensorflow Sonstige
Vortrag auf DEBRL2019 (Digital Exchange Bergisches Rheinland 2019), 2019.
@misc{Buhl19,
title = {Deep Learning mit Keras und Tensorflow},
author = {Henning Buhl and Wolfgang Konen and Markus Thill},
url = {https://github.com/HenningBuhl/DLKTF},
year = {2019},
date = {2019-01-01},
howpublished = {Vortrag auf DEBRL2019 (Digital Exchange Bergisches Rheinland 2019)},
keywords = {deep learning},
pubstate = {published},
tppubtype = {misc}
}
Barsnick, Felix
Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen Abschlussarbeit
TH Köln -- University of Applied Sciences, 2019, (Master thesis).
@mastersthesis{Barsnick2019,
title = {Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen},
author = {Felix Barsnick},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_MMI_Barsnick-2019-04-final.pdf},
year = {2019},
date = {2019-01-01},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Master thesis},
keywords = {BT-MT, Elo, Game Learning, GBG, Glicko, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Thill, Markus; Däubner, Sina; Konen, Wolfgang; Bäck, Thomas
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks Proceedings Article
In: á, Petra Barancíkov; Holena, Martin; others, (Hrsg.): Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019), 2019, (Best Paper Award).
@inproceedings{Thill2019a,
title = {Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks},
author = {Markus Thill and Sina Däubner and Wolfgang Konen and Thomas Bäck},
editor = {Petra Barancíkov á and Martin Holena and others},
url = {http://ceur-ws.org/Vol-2473},
year = {2019},
date = {2019-01-01},
booktitle = {Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019)},
volume = {2473},
series = {CEUR Workshop Proceedings},
note = {Best Paper Award},
keywords = {anomaly detection, deep learning, LSTM, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas
How to Solve the Dilemma of Margin-Based Equality Handling Methods Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke; Mikut, Ralf (Hrsg.): Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018, S. 257-270, KIT Scientific Publishing, Karlsruhe, 2018, ISBN: 978-3-7315-0845-8, (Young Author Award).
@inproceedings{Bagheri2018,
title = {How to Solve the Dilemma of Margin-Based Equality Handling Methods},
author = {Samineh Bagheri and Wolfgang Konen and Thomas Bäck },
editor = {Frank Hoffmann and Eyke Hüllermeier and Ralf Mikut },
url = {https://blogs.gm.fh-koeln.de/ciop/files/2018/12/GMA2018.pdf},
doi = {10.5445/KSP/1000085935},
isbn = {978-3-7315-0845-8},
year = {2018},
date = {2018-11-29},
booktitle = {Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018},
pages = {257-270},
publisher = {KIT Scientific Publishing, Karlsruhe},
note = {Young Author Award},
keywords = {equality constraint, optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {inproceedings}
}
Thill, Markus; Konen, Wolfgang; Bäck, Thomas
Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2018, (submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf).
@techreport{Thill2018,
title = {Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions},
author = {Markus Thill and Wolfgang Konen and Thomas Bäck},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Thill18.d/AoDS2018.pdf},
year = {2018},
date = {2018-11-01},
address = {TH Köln - University of Applied Science},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
note = {submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf},
keywords = {anomaly detection, DWT, maximum likelihood estimation, time series, wavelet transform},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang; Koch, Patrick
The TDMR 2.0 Package: Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, Nr. 02/2018, 2018, (Last update: April 2018 (original version: 2012)).
@techreport{Kone18a,
title = {The TDMR 2.0 Package: Tuned Data Mining in R},
author = {Wolfgang Konen and Patrick Koch},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone12a.d/Kone12a.pdf},
year = {2018},
date = {2018-04-01},
number = {02/2018},
address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
note = {Last update: April 2018 (original version: 2012)},
keywords = {parameter tuning, SOMA, TDMR},
pubstate = {published},
tppubtype = {techreport}
}
Konen, Wolfgang; Koch, Patrick
The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, Nr. 03/2018, 2018, (Last update: April 2018 (original version: 2012)).
@techreport{Kone18b,
title = {The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R},
author = {Wolfgang Konen and Patrick Koch},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone12b.d/Kone12b.pdf},
year = {2018},
date = {2018-04-01},
number = {03/2018},
address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
note = {Last update: April 2018 (original version: 2012)},
keywords = {parameter tuning, SOMA, TDMR},
pubstate = {published},
tppubtype = {techreport}
}
2017
Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas
Comparing Kriging and Radial Basis Function Surrogates Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 243-259, Universitätsverlag Karlsruhe, 2017.
@inproceedings{Bagh17c,
title = {Comparing Kriging and Radial Basis Function Surrogates},
author = {Samineh Bagheri and Wolfgang Konen and Thomas Bäck},
editor = {Frank Hoffmann and Eyke Hüllermeier},
url = {http://blogs.gm.fh-koeln.de/ciop/files/2018/10/Bagheri2017-1.pdf
https://publikationen.bibliothek.kit.edu/1000074341
https://www.researchgate.net/publication/328222483_Comparing_Kriging_and_Radial_Basis_Function_Surrogates
},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings 27. Workshop Computational Intelligence},
pages = {243-259},
publisher = {Universitätsverlag Karlsruhe},
keywords = {CI, constraints, Kriging, MONREP, optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {inproceedings}
}
Thill, Markus; Konen, Wolfgang; Bäck, Thomas
Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017.
@inproceedings{Thill2017c,
title = {Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation},
author = {Markus Thill and Wolfgang Konen and Thomas Bäck},
editor = {Frank Hoffmann and Eyke Hüllermeier},
url = {https://publikationen.bibliothek.kit.edu/1000074341},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings 27. Workshop Computational Intelligence},
pages = {67-71},
publisher = {Universitätsverlag Karlsruhe},
keywords = {anomaly detection, DWT, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Bagheri, Samineh; Konen, Wolfgang; Allmendinger, Richard; Branke, Jürgen; Deb, Kalyanmoy; Fieldsend, Jonathan; Quagliarella, Domenico; Sindhya, Karthik
Constraint Handling in Efficient Global Optimization Proceedings Article
In: Bosman, Peter A N (Hrsg.): Genetic and Evolutionary Computation Conference 2017 (GECCO'17), Berlin, S. 1, ACM, 2017.
@inproceedings{Bagh17a,
title = {Constraint Handling in Efficient Global Optimization},
author = {Samineh Bagheri and Wolfgang Konen and Richard Allmendinger and Jürgen Branke and Kalyanmoy Deb and Jonathan Fieldsend and Domenico Quagliarella and Karthik Sindhya},
editor = {Peter A N Bosman},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/Bagh17-GECCO.pdf},
year = {2017},
date = {2017-07-01},
booktitle = {Genetic and Evolutionary Computation Conference 2017 (GECCO'17), Berlin},
pages = {1},
publisher = {ACM},
keywords = {CI, constraints, Kriging, MONREP, optimization, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {inproceedings}
}
Thill, Markus; Konen, Wolfgang; Bäck, Thomas
Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation Proceedings Article
In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017.
@inproceedings{Thill17b-ITISE,
title = {Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation},
author = {Markus Thill and Wolfgang Konen and Thomas Bäck},
editor = {Olga Valenzuela and Ignacio Rojas and others},
url = {http://blogs.gm.fh-koeln.de/ciop/files/2019/01/thillwavelet.pdf},
year = {2017},
date = {2017-07-01},
booktitle = {International Work-Conference on Time Series (ITISE2017)},
keywords = {anomaly detection, DWT, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
265 Einträge « ‹ 1 von 6
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Search Field
265 Einträge « ‹ 1 von 27
› » 1.
Engelhardt, Raphael; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Exploring the Reliability of SHAP Values in Reinforcement Learning Proceedings Article
In: Longo, Luca; Lapuschkin, Sebastian; Seifert, Christin (Hrsg.): Explainable Artificial Intelligence: Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III, S. 165–184, Springer, Cham, 2024, ISBN: 978-3-031-63799-5.
@inproceedings{Engelhardt2024b,
title = {Exploring the Reliability of SHAP Values in Reinforcement Learning},
author = {Raphael Engelhardt and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Luca Longo and Sebastian Lapuschkin and Christin Seifert},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Engelh24a.d/Evaluation_of_SHAP_for_RL_XAI2024.pdf},
doi = {10.1007/978-3-031-63800-8},
isbn = {978-3-031-63799-5},
year = {2024},
date = {2024-07-09},
booktitle = {Explainable Artificial Intelligence: Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III},
volume = {2155},
pages = {165–184},
publisher = {Springer},
address = {Cham},
series = {Communications in Computer and Information Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Engelhardt, Raphael C; Meinen, Marcel J; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task Artikel
In: arXiv preprint arXiv:2412.04974, 2024.
@article{Engelhardt2024,
title = {Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task},
author = {Raphael C Engelhardt and Marcel J Meinen and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
url = {https://arxiv.org/pdf/2412.04974},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {arXiv preprint arXiv:2412.04974},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3.
Lange, Moritz; Engelhardt, Raphael C; Konen, Wolfgang; Wiskott, Laurenz
Beyond Trial and Error in Reinforcement Learning Proceedings Article
In: Proceedings of the DataNinja sAIOnARA 2024 Conference, S. 58, 2024.
@inproceedings{Lange2024,
title = {Beyond Trial and Error in Reinforcement Learning},
author = {Moritz Lange and Raphael C Engelhardt and Wolfgang Konen and Laurenz Wiskott},
url = {https://biecoll.ub.uni-bielefeld.de/index.php/dataninja/issue/download/82/1#page=63},
doi = {10.11576/dataninja-1156},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the DataNinja sAIOnARA 2024 Conference},
pages = {58},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Lange, Moritz; Engelhardt, Raphael C; Konen, Wolfgang; Wiskott, Laurenz
Interpretable brain-inspired representations improve RL performance on visual navigation tasks Artikel
In: arXiv preprint arXiv:2402.12067, 2024.
@article{Lange2024a,
title = {Interpretable brain-inspired representations improve RL performance on visual navigation tasks},
author = {Moritz Lange and Raphael C Engelhardt and Wolfgang Konen and Laurenz Wiskott},
url = {https://arxiv.org/pdf/2402.12067},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {arXiv preprint arXiv:2402.12067},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
5.
Engelhardt, Raphael C.; Raycheva, Ralitsa; Lange, Moritz; Wiskott, Laurenz; Konen, Wolfgang
Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers, Springer Nature Switzerland, Imprint: Springer, 2024.
@inproceedings{Engelhardt2024a,
title = {Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning},
author = {Raphael C. Engelhardt and Ralitsa Raycheva and Moritz Lange and Laurenz Wiskott and Wolfgang Konen},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
url = {https://www.gm.fh-koeln.de/ciopwebpub/Engelh23a.d/Engelh23a.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers},
publisher = {Springer Nature Switzerland, Imprint: Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
6.
Lange, Moritz; Krystiniak, Noah; Engelhardt, Raphael C.; Konen, Wolfgang; Wiskott, Laurenz
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers, Springer Nature Switzerland, Imprint: Springer, 2024, (Best Paper Award).
@inproceedings{Lange2024b,
title = {Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison},
author = {Moritz Lange and Noah Krystiniak and Raphael C. Engelhardt and Wolfgang Konen and Laurenz Wiskott},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
url = {https://link.springer.com/book/10.1007/978-3-031-53969-5},
year = {2024},
date = {2024-01-01},
booktitle = {Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers},
publisher = {Springer Nature Switzerland, Imprint: Springer},
note = {Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
7.
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: arXiv preprint arXiv:2405.15512, 2024.
@article{Oedingen2024,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://arxiv.org/abs/2405.15512},
year = {2024},
date = {2024-01-01},
journal = {arXiv preprint arXiv:2405.15512},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
8.
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: AI, Bd. 5, Nr. 3, S. 1066–1094, 2024, ISSN: 2673-2688.
@article{Oedingen2024a,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://www.mdpi.com/2673-2688/5/3/53},
doi = {10.3390/ai5030053},
issn = {2673-2688},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {AI},
volume = {5},
number = {3},
pages = {1066–1094},
abstract = {In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.
9.
Lange, Moritz; Krystiniak, Noah; Engelhardt, Raphael C.; Konen, Wolfgang; Wiskott, Laurenz
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison Proceedings Article
In: Nicosia, Giuseppe; Pardalos, Panos; others, (Hrsg.): 9th International Conference on machine Learning, Optimization, and Data Science (LOD2023), 2023.
@inproceedings{Lange2023,
title = {Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison},
author = {Moritz Lange and Noah Krystiniak and Raphael C. Engelhardt and Wolfgang Konen and Laurenz Wiskott},
editor = {Giuseppe Nicosia and Panos Pardalos and others},
year = {2023},
date = {2023-01-01},
booktitle = {9th International Conference on machine Learning, Optimization, and Data Science (LOD2023)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
10.
Konen, Wolfgang
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning Artikel
In: arXiv preprint arXiv:2301.12167, 2023.
@article{Konen2023,
title = {Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning},
author = {Wolfgang Konen},
url = {https://arxiv.org/abs/2301.12167},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {arXiv preprint arXiv:2301.12167},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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