Kyrill Schmid
Kyrill Schmid
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Zitiert von
Zitiert von
Analysis of feature representations for anomalous sound detection
R Müller, S Illium, F Ritz, K Schmid
arXiv preprint arXiv:2012.06282, 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
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
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
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
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
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
Stochastic market games
K Schmid, L Belzner, R Müller, J Tochtermann, C Linnhoff-Popien
arXiv preprint arXiv:2207.07388, 2022
Learning to participate through trading of reward shares
M Kölle, T Matheis, P Altmann, K Schmid
arXiv preprint arXiv:2301.07416, 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
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
Learning to penalize other learning agents
K Schmid, L Belzner, C Linnhoff-Popien
Artificial Life Conference Proceedings 33 2021 (1), 59, 2021
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
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
Enthalpy of denaturation for human hemoglobin in the oxygenated and deoxygenated state
RG Müller, K Schmid
Thermochimica Acta 69 (1-2), 115-125, 1983
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines
T Müller, C Roch, K Schmid, P Altmann
arXiv preprint arXiv:2109.10900, 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
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
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
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
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