| Analysis of feature representations for anomalous sound detection R Müller, S Illium, F Ritz, K Schmid arXiv preprint arXiv:2012.06282, 2020 | 21 | 2020 |
| Leveraging statistical multi-agent online planning with emergent value function approximation T Phan, L Belzner, T Gabor, K Schmid arXiv preprint arXiv:1804.06311, 2018 | 19 | 2018 |
| Automated recognition and difficulty assessment of boulder routes A Ebert, K Schmid, C Marouane, C Linnhoff-Popien Internet of Things (IoT) Technologies for HealthCare: 4th International …, 2018 | 17 | 2018 |
| Preparing for the unexpected: Diversity improves planning resilience in evolutionary algorithms T Gabor, L Belzner, T Phan, K Schmid 2018 IEEE international conference on autonomic computing (ICAC), 131-140, 2018 | 16 | 2018 |
| Action markets in deep multi-agent reinforcement learning K Schmid, L Belzner, T Gabor, T Phan Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 12 | 2018 |
| Memory bounded open-loop planning in large pomdps using thompson sampling T Phan, L Belzner, M Kiermeier, M Friedrich, K Schmid, C Linnhoff-Popien Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7941-7948, 2019 | 11 | 2019 |
| Distributed policy iteration for scalable approximation of cooperative multi-agent policies T Phan, K Schmid, L Belzner, T Gabor, S Feld, C Linnhoff-Popien arXiv preprint arXiv:1901.08761, 2019 | 7 | 2019 |
| Stochastic market games K Schmid, L Belzner, R Müller, J Tochtermann, C Linnhoff-Popien arXiv preprint arXiv:2207.07388, 2022 | 6 | 2022 |
| Learning to participate through trading of reward shares M Kölle, T Matheis, P Altmann, K Schmid arXiv preprint arXiv:2301.07416, 2023 | 5 | 2023 |
| Distributed emergent agreements with deep reinforcement learning K Schmid, R Müller, L Belzner, J Tochtermann, C Linhoff-Popien 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 5 | 2021 |
| Learning to penalize other learning agents K Schmid, L Belzner, C Linnhoff-Popien Artificial Life Conference Proceedings 33 2021 (1), 59, 2021 | 4 | 2021 |
| Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information. K Schmid, L Belzner, T Phan, T Gabor, C Linnhoff-Popien ICAART (1), 144-151, 2020 | 4 | 2020 |
| Difficulty Classification of Mountainbike Downhill Trails Utilizing Deep Neural Networks S Langer, R Müller, K Schmid, C Linnhoff-Popien Machine Learning and Knowledge Discovery in Databases: International …, 2020 | 3 | 2020 |
| The sharer’s dilemma in collective adaptive systems of self-interested agents L Belzner, K Schmid, T Phan, T Gabor, M Wirsing Leveraging Applications of Formal Methods, Verification and Validation …, 2018 | 3 | 2018 |
| Enthalpy of denaturation for human hemoglobin in the oxygenated and deoxygenated state RG Müller, K Schmid Thermochimica Acta 69 (1-2), 115-125, 1983 | 3 | 1983 |
| Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines T Müller, C Roch, K Schmid, P Altmann arXiv preprint arXiv:2109.10900, 2021 | 2 | 2021 |
| Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion T Müller, K Schmid, D Schuman, T Gabor, M Friedrich, M Geitz arXiv preprint arXiv:2109.10617, 2021 | 2 | 2021 |
| A Distributed Policy Iteration Scheme for Cooperative Multi-Agent Policy Approximation T Phan, L Belzner, K Schmid, T Gabor, F Ritz, S Feld, C Linnhoff-Popien 12th Adaptive and Learning Agents Workshop (ALA’20), 2020 | 2 | 2020 |
| On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives X Sun, FR Pieroth, K Schmid, M Wirsing, L Belzner 2022 IEEE 42nd International Conference on Distributed Computing Systems …, 2022 | 1 | 2022 |
| Risk-sensitivity in simulation based online planning K Schmid, L Belzner, M Kiermeier, A Neitz, T Phan, T Gabor, C Linnhoff KI 2018: Advances in Artificial Intelligence: 41st German Conference on AI …, 2018 | 1 | 2018 |
