TY - CHAP A1 - Schmid, Kyrill A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Learning to penalize other learning agents T2 - Proceedings of the Artificial Life Conference 2021 UR - https://doi.org/10.1162/isal_a_00369 Y1 - 2021 UR - https://doi.org/10.1162/isal_a_00369 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12966 VL - 2021 PB - MIT Press CY - Cambridge ER - TY - CHAP A1 - Belzner, Lenz A1 - Schmid, Kyrill A1 - Phan, Thomy A1 - Gabor, Thomas A1 - Wirsing, Martin T1 - The sharer’s dilemma in collective adaptive systems of self-interested agents T2 - Leveraging Applications of Formal Methods, Verification and Validation : Distributed Systems : 8th International Symposium, ISoLA 2018; Limassol, Cyprus, November 5–9, 2018; Proceedings, Part III UR - https://doi.org/10.1007/978-3-030-03424-5_16 Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-03424-5_16 SN - 978-3-030-03423-8 SN - 978-3-030-03424-5 SP - 241 EP - 256 PB - Springer CY - Cham ER - TY - CHAP A1 - Schmid, Kyrill A1 - Müller, Robert A1 - Belzner, Lenz A1 - Tochtermann, Johannes A1 - Linnhoff-Popien, Claudia T1 - Distributed emergent agreements with deep reinforcement learning T2 - 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings UR - https://doi.org/10.1109/IJCNN52387.2021.9533333 KW - Waste materials KW - Neural networks KW - Buildings KW - Reinforcement learning KW - Production facilities KW - Robustness KW - Autonomous agents Y1 - 2021 UR - https://doi.org/10.1109/IJCNN52387.2021.9533333 SN - 978-1-6654-3900-8 SN - 2161-4407 PB - IEEE ER - TY - CHAP A1 - Schmid, Kyrill A1 - Belzner, Lenz A1 - Phan, Thomy A1 - Gabor, Thomas A1 - Linnhoff-Popien, Claudia ED - Rocha, Ana ED - Steels, Luc ED - Herik, Jaap van den T1 - Multi-agent reinforcement learning for bargaining under risk and asymmetric information T2 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART 2020 N2 - In cooperative game theory bargaining games refer to situations where players can agree to any one of a variety of outcomes but there is a conflict on which specific outcome to choose. However, the players cannot impose a specific outcome on others and if no agreement is reached all players receive a predetermined status quo outcome. Bargaining games have been studied from a variety of fields, including game theory, economics, psychology and simulation based methods like genetic algorithms. In this work we extend the analysis by means of deep multi-agent reinforcement learning (MARL). To study the dynamics of bargaining with reinforcement learning we propose two different bargaining environments which display the following situations: in the first domain two agents have to agree on the division of an asset, e.g., the division of a fixed amount of money between each other. The second domain models a seller-buyer scenario in which agents must agree on a price for a product. We empirica lly demonstrate that the bargaining result under MARL is influenced by agents’ risk-aversion as well as information asymmetry between agents. UR - https://doi.org/10.5220/0008913901440151 Y1 - 2020 UR - https://doi.org/10.5220/0008913901440151 SN - 978-989-758-395-7 SN - 2184-433X SP - 144 EP - 151 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Phan, Thomy A1 - Belzner, Lenz A1 - Kiermeier, Marie A1 - Friedrich, Markus A1 - Schmid, Kyrill A1 - Linnhoff-Popien, Claudia T1 - Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling T2 - AAAI-19 / IAAI-19 / EAAI-20 Proceedings UR - https://doi.org/10.1609/aaai.v33i01.33017941 Y1 - 2019 UR - https://doi.org/10.1609/aaai.v33i01.33017941 SN - 2374-3468 VL - 33 IS - 1 SP - 7941 EP - 7948 PB - AAAI Press CY - Palo Alto ER - TY - CHAP A1 - Phan, Thomy A1 - Schmid, Kyrill A1 - Belzner, Lenz A1 - Gabor, Thomas A1 - Feld, Sebastian A1 - Linnhoff-Popien, Claudia T1 - Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies T2 - AAMAS '19 : Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems KW - multi-agent planning KW - multi-agent learning KW - policy iteration Y1 - 2019 UR - https://www.ifaamas.org/Proceedings/aamas2019/forms/contents.htm SN - 978-1-4503-6309-9 SP - 2162 EP - 2164 PB - International Foundation for Autonomous Agents and Multiagent Systems CY - Richland ER - TY - CHAP A1 - Schmid, Kyrill A1 - Belzner, Lenz A1 - Kiermeier, Marie A1 - Neitz, Alexander A1 - Phan, Thomy A1 - Gabor, Thomas A1 - Linnhoff-Popien, Claudia ED - Trollmann, Frank ED - Turhan, Anni-Yasmin T1 - Risk-sensitivity in simulation based online planning T2 - KI 2018: Advances in Artificial Intelligence : Proceedings UR - https://doi.org/10.1007/978-3-030-00111-7_20 KW - online planning KW - risk-sensitivity KW - local planning Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-00111-7_20 SN - 978-3-030-00111-7 SN - 978-3-030-00110-0 SP - 229 EP - 240 PB - Springer CY - Cham ER - TY - CHAP A1 - Gabor, Thomas A1 - Belzner, Lenz A1 - Phan, Thomy A1 - Schmid, Kyrill T1 - Preparing for the unexpected BT - diversity improves planning resilience in evolutionary algorithms T2 - Proceedings : 15th IEEE International Conference on Autonomic Computing - ICAC 2018 UR - https://doi.org/10.1109/ICAC.2018.00023 KW - planning KW - unexpected events KW - dynamic fitness KW - resilience KW - robustness KW - self-protection KW - self-healing KW - diversity KW - optimization KW - evolutionary algorithms Y1 - 2018 UR - https://doi.org/10.1109/ICAC.2018.00023 SN - 978-1-5386-5139-1 SN - 2474-0756 SP - 131 EP - 140 PB - IEEE CY - Los Alamitos (CA) ER - TY - CHAP A1 - Schmid, Kyrill A1 - Belzner, Lenz A1 - Gabor, Thomas A1 - Phan, Thomy ED - Kurkova, Vera ED - Manolopoulos, Yannis ED - Hammer, Barbara ED - Iliadis, Lazaros ED - Maglogiannis, Ilias T1 - Action markets in deep multi-agent reinforcement learning T2 - Artificial Neural Networks and Machine Learning – ICANN 2018 : Proceedings, Part II UR - https://doi.org/10.1007/978-3-030-01421-6_24 Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-01421-6_24 SN - 978-3-030-01421-6 SN - 978-3-030-01420-9 SP - 240 EP - 249 PB - Springer CY - Cham ER - TY - CHAP A1 - Sun, Xiyue A1 - Pieroth, Fabian Raoul A1 - Schmid, Kyrill A1 - Wirsing, Martin A1 - Belzner, Lenz T1 - On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives T2 - Proceedings: 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops: ICDCSW 2022 UR - https://doi.org/10.1109/ICDCSW56584.2022.00031 KW - Prisoner’s Dilemma KW - Cooperation KW - Incentivizing KW - Reinforcement Learning Y1 - 2022 UR - https://doi.org/10.1109/ICDCSW56584.2022.00031 SN - 978-1-6654-8879-2 SN - 2332-5666 SP - 113 EP - 118 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Phan, Thomy A1 - Belzner, Lenz A1 - Gabor, Thomas A1 - Schmid, Kyrill T1 - Leveraging statistical multi-agent online planning with emergent value function approximation T2 - AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems KW - multi-agent planning KW - online planning KW - value function approximation Y1 - 2018 UR - https://www.ifaamas.org/Proceedings/aamas2018/forms/contents.htm#18 SN - 978-1-4503-5649-7 SN - 2523-5699 SP - 730 EP - 738 PB - International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS) CY - Richland ER - TY - CHAP A1 - Schmid, Kyrill A1 - Belzner, Lenz A1 - Müller, Robert A1 - Tochtermann, Johannes A1 - Linnhoff-Popien, Claudia T1 - Stochastic market games T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence UR - https://doi.org/10.24963/ijcai.2021/54 Y1 - 2021 UR - https://doi.org/10.24963/ijcai.2021/54 SN - 978-0-9992411-9-6 SP - 384 EP - 390 PB - IJCAI CY - [s. l.] ER -