TY - CHAP A1 - Ritz, Fabian A1 - Ratke, Daniel A1 - Phan, Thomy A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - A sustainable ecosystem through emergent cooperation in multi-agent reinforcement learning T2 - Proceedings of the Artificial Life Conference 2021 UR - https://doi.org/10.1162/isal_a_00399 Y1 - 2021 UR - https://doi.org/10.1162/isal_a_00399 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12975 VL - 2021 PB - MIT Press CY - Cambridge ER - TY - JOUR A1 - Waschneck, Bernd A1 - Reichstaller, André A1 - Belzner, Lenz A1 - Altenmüller, Thomas A1 - Bauernhansl, Thomas A1 - Knapp, Alexander A1 - Kyek, Andreas T1 - Optimization of global production scheduling with deep reinforcement learning JF - Procedia CIRP N2 - Industrie 4.0 introduces decentralized, self-organizing and self-learning systems for production control. At the same time, new machine learning algorithms are getting increasingly powerful and solve real world problems. We apply Google DeepMind’s Deep Q Network (DQN) agent algorithm for Reinforcement Learning (RL) to production scheduling to achieve the Industrie 4.0 vision for production control. In an RL environment cooperative DQN agents, which utilize deep neural networks, are trained with user-defined objectives to optimize scheduling. We validate our system with a small factory simulation, which is modeling an abstracted frontend-of-line semiconductor production facility. UR - https://doi.org/10.1016/j.procir.2018.03.212 KW - Production Scheduling KW - Reinforcement Learning KW - Machine Learning in Manufacturing Y1 - 2018 UR - https://doi.org/10.1016/j.procir.2018.03.212 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-15735 SN - 2212-8271 VL - 2018 IS - 72 SP - 1264 EP - 1269 PB - Elsevier CY - Amsterdam ER - 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 - Phan, Thomy A1 - Ritz, Fabian A1 - Belzner, Lenz A1 - Altmann, Philipp A1 - Gabor, Thomas A1 - Linnhoff-Popien, Claudia T1 - VAST: Value Function Factorization with Variable Agent Sub-Teams T2 - Advances in Neural Information Processing Systems 34 (NeurIPS 2021) KW - Multi-Agent Learning KW - Reinforcement Learning KW - Value Function Factorization Y1 - 2021 UR - https://proceedings.neurips.cc/paper/2021/hash/c97e7a5153badb6576d8939469f58336-Abstract.html PB - Neural Information Processing Systems Foundation, Inc. (NIPS) 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 - Unold, Florian von A1 - Wintergerst, Monika A1 - Belzner, Lenz A1 - Groh, Georg T1 - DYME: a dynamic metric for dialog modeling learned from human conversations T2 - Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part V UR - https://doi.org/10.1007/978-3-030-92307-5_30 KW - Dialog modeling KW - Conversational metrics KW - Dialog systems KW - Natural language processing Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-92307-5_30 SN - 978-3-030-92307-5 SP - 257 EP - 264 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 - JOUR A1 - Gabor, Thomas A1 - Illium, Steffen A1 - Zorn, Maximilian A1 - Lenta, Cristian A1 - Mattausch, Andy A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Self-Replication in Neural Networks JF - Artificial Life UR - https://doi.org/10.1162/artl_a_00359 KW - neural network KW - self-replication KW - artificial chemistry system KW - soup KW - weight space Y1 - 2022 UR - https://doi.org/10.1162/artl_a_00359 SN - 1530-9185 VL - 28 IS - 2 SP - 205 EP - 223 PB - MIT Press CY - Cambridge ER - TY - JOUR A1 - Gabor, Thomas A1 - Sedlmeier, Andreas A1 - Phan, Thomy A1 - Ritz, Fabian A1 - Kiermeier, Marie A1 - Belzner, Lenz A1 - Kempter, Bernhard A1 - Klein, Cornel A1 - Sauer, Horst A1 - Schmid, Reiner A1 - Wieghardt, Jan A1 - Zeller, Marc A1 - Linnhoff-Popien, Claudia T1 - The scenario coevolution paradigm BT - adaptive quality assurance for adaptive systems JF - International Journal on Software Tools for Technology Transfer N2 - Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137–154, 2018). UR - https://doi.org/10.1007/s10009-020-00560-5 Y1 - 2020 UR - https://doi.org/10.1007/s10009-020-00560-5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13929 SN - 1433-2787 VL - 22 IS - 4 SP - 457 EP - 476 PB - Springer CY - Berlin 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 - Sedlmeier, Andreas A1 - Gabor, Thomas A1 - Phan, Thomy A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia ED - Rocha, Ana ED - Steels, Luc ED - Herik, Jaap van den T1 - Uncertainty-based out-of-distribution classification in deep reinforcement learning T2 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART N2 - Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. As a first step towards a solution, we consider the problem of detecting such data in a value-based deep reinforcement learning (RL) setting. Modelling this problem as a one-class classification problem, we propose a framework for uncertainty-based OOD classification: UBOOD. It is based on the effect that an agent’s epistemic uncertainty is reduced for situations encountered during training (in-distribution), and thus lower than for unencountered (OOD) situations. Being agnostic towards the approach used for estimating epistemic uncertainty, combinations with different uncertainty estimation methods, e.g. approximate Bayesian inference methods or ensembling techniques are possible. Evaluation shows that the framework produces reliable classification results when combined with ensemble-based estimators, while the combination with concrete dropout-based estimators fails to r eliably detect OOD situations. UR - https://doi.org/10.5220/0008949905220529 KW - Uncertainty in AI KW - Out-of-Distribution Classification KW - Deep Reinforcement Learning Y1 - 2020 UR - https://doi.org/10.5220/0008949905220529 SN - 978-989-758-395-7 SN - 2184-433X SP - 522 EP - 529 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Gabor, Thomas A1 - Sünkel, Leo A1 - Ritz, Fabian A1 - Phan, Thomy A1 - Belzner, Lenz A1 - Roch, Christoph A1 - Feld, Sebastian A1 - Linnhoff-Popien, Claudia T1 - The holy grail of quantum artificial intelligence BT - major challenges in accelerating the machine learning pipeline T2 - ICSEW'20 : Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops UR - https://doi.org/10.1145/3387940.3391469 KW - quantum computing KW - artificial intelligence KW - software engineering Y1 - 2020 UR - https://doi.org/10.1145/3387940.3391469 SN - 978-1-4503-7963-2 SP - 456 EP - 461 PB - ACM CY - New York ER - TY - CHAP A1 - Phan, Thomy A1 - Belzner, Lenz A1 - Gabor, Thomas A1 - Sedlmeier, Andreas A1 - Ritz, Fabian A1 - Linnhoff-Popien, Claudia T1 - Resilient multi-agent reinforcement learning with adversarial value decomposition T2 - AAAI-21 / IAAI-21 / EAAI-21 Proceedings UR - https://ojs.aaai.org/index.php/AAAI/article/view/17348 KW - Multiagent Learning KW - Adversarial Learning & Robustness KW - Adversarial Agents KW - Reinforcement Learning Y1 - 2021 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17348 SN - 978-1-57735-866-4 SN - 2374-3468 VL - 35 IS - 13 SP - 11308 EP - 11316 PB - AAAI Press CY - Palo Alto (CA) ER - TY - CHAP A1 - Feld, Sebastian A1 - Sedlmeier, Andreas A1 - Friedrich, Markus A1 - Franz, Jan A1 - Belzner, Lenz T1 - Bayesian Surprise in Indoor Environments T2 - SIGSPATIAL '19 : Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems UR - https://doi.org/10.1145/3347146.3359358 KW - Bayesian Surprise KW - Novelty KW - Salience KW - Isovist Analysis KW - Indoor Location- Based Service KW - Indoor LBS KW - Indoor Navigation KW - Trajectory Characterization Y1 - 2019 UR - https://doi.org/10.1145/3347146.3359358 SN - 978-1-4503-6909-1 PB - ACM CY - New York 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 - JOUR A1 - Posor, Jorrit Enzio A1 - Belzner, Lenz A1 - Knapp, Alexander T1 - Joint Action Learning for Multi-Agent Cooperation using Recurrent Reinforcement Learning JF - Digitale Welt UR - https://doi.org/10.1007/s42354-019-0239-y Y1 - 2019 UR - https://doi.org/10.1007/s42354-019-0239-y SN - 2569-1996 VL - 4 IS - 1 SP - 79 EP - 84 PB - Digitale Welt Academy CY - München ER - TY - CHAP A1 - Pieroth, Fabian Raoul A1 - Fitch, Katherine A1 - Belzner, Lenz T1 - Detecting Influence Structures in Multi-Agent Reinforcement Learning Systems KW - multi-agent reinforcement learning Y1 - 2022 UR - http://aaai-rlg.mlanctot.info/sched.html#poster1 ER - TY - JOUR A1 - Belzner, Lenz A1 - Wirsing, Martin T1 - Synthesizing safe policies under probabilistic constraints with reinforcement learning and Bayesian model checking JF - Science of Computer Programming UR - https://doi.org/10.1016/j.scico.2021.102620 KW - Policy synthesis KW - Constrained Markov decision process KW - Safe reinforcement learning KW - Bayesian model checking KW - Probabilistic constraints Y1 - 2021 UR - https://doi.org/10.1016/j.scico.2021.102620 SN - 0167-6423 VL - 2021 IS - 206 PB - Elsevier CY - Amsterdam 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 - Linnhoff-Popien, Claudia ED - Aguirre, Hernan T1 - Inheritance-based diversity measures for explicit convergence control in evolutionary algorithms T2 - GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference UR - https: //doi.org/10.1145/3205455.3205630 KW - diversity KW - evolutionary algorithms KW - premature convergence KW - optimization KW - genetic drift Y1 - 2018 UR - https: //doi.org/10.1145/3205455.3205630 SN - 978-1-4503-5618-3 SP - 841 EP - 848 PB - ACM CY - New York ER - TY - CHAP A1 - Feld, Sebastian A1 - Sedlmeier, Andreas A1 - Illium, Steffen A1 - Belzner, Lenz T1 - Trajectory annotation using sequences of spatial perception T2 - SIGSPATIAL '18 : Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems UR - https://doi.org/10.1145/3274895.3274968 KW - Spatial Syntax KW - Isovist Analysis KW - Geospatial Trajectories KW - Indoor Navigation KW - Auto-Encoder KW - Artificial Neural Networks Y1 - 2018 UR - https://doi.org/10.1145/3274895.3274968 SN - 978-1-4503-5889-7 SP - 329 EP - 338 PB - ACM CY - New York 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 - Hahn, Carsten A1 - Phan, Thomy A1 - Gabor, Thomas A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Emergent Escape-based Flocking behavior using Multi-Agent Reinforcement Learning T2 - Artificial Life Conference Proceedings UR - https://doi.org/10.1162/isal_a_00226 Y1 - 2019 UR - https://doi.org/10.1162/isal_a_00226 SP - 598 EP - 605 PB - MIT Press CY - Cambridge (MA) ER - TY - JOUR A1 - Sedlmeier, Andreas A1 - Gabor, Thomas A1 - Phan, Thomy A1 - Belzner, Lenz T1 - Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning JF - Digitale Welt UR - https://doi.org/10.1007/s42354-019-0238-z Y1 - 2019 UR - https://doi.org/10.1007/s42354-019-0238-z SN - 2569-1996 VL - 4 IS - 1 SP - 74 EP - 78 PB - Digitale Welt Academy CY - München ER - TY - CHAP A1 - Gabor, Thomas A1 - Illium, Steffen A1 - Mattausch, Andy A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Self-Replication in Neural Networks T2 - Artificial Life Conference Proceedings UR - https://doi.org/10.1162/isal_a_00197 Y1 - 2019 UR - https://doi.org/10.1162/isal_a_00197 SP - 424 EP - 431 PB - MIT Press CY - Cambridge ER - TY - CHAP A1 - Wirsing, Martin A1 - Belzner, Lenz ED - Lopez-Garcia, Pedro ED - Gallagher, John P. ED - Giacobazzi, Roberto T1 - Towards Systematically Engineering Autonomous Systems Using Reinforcement Learning and Planning T2 - Analysis, Verification and Transformation for Declarative Programming and Intelligent Systems UR - https://doi.org/10.1007/978-3-031-31476-6_16 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-31476-6_16 SN - 978-3-031-31476-6 SN - 978-3-031-31475-9 SP - 281 EP - 306 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 - Sommer, Felix A1 - Altmann, Philipp A1 - Ritz, Fabian A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Emergent Cooperation from Mutual Acknowledgment Exchange T2 - Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022) KW - multi-agent learning KW - reinforcement learning KW - mutual acknowledgments KW - peer incentivization KW - emergent cooperation Y1 - 2022 UR - https://dl.acm.org/doi/10.5555/3535850.3535967 SN - 978-1-4503-9213-6 SP - 1047 EP - 1055 PB - International Foundation for Autonomous Agents and Multiagent Systems CY - Richland ER - TY - CHAP A1 - Waschneck, Bernd A1 - Reichstaller, André A1 - Belzner, Lenz A1 - Altenmüller, Thomas A1 - Bauernhansl, Thomas A1 - Knapp, Alexander A1 - Kyek, Andreas T1 - Deep reinforcement learning for semiconductor production scheduling T2 - 2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) UR - https://doi.org/10.1109/ASMC.2018.8373191 KW - Production Scheduling KW - Reinforcement Learning KW - Machine Learning KW - Semiconductor Manufacturing Y1 - 2018 UR - https://doi.org/10.1109/ASMC.2018.8373191 SN - 978-1-5386-3748-7 SN - 2376-6697 SP - 301 EP - 306 PB - IEEE CY - Piscataway 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 - Gabor, Thomas A1 - Kiermeier, Marie A1 - Belzner, Lenz ED - Linnhoff-Popien, Claudia ED - Schneider, Ralf ED - Zaddach, Michael T1 - Don’t Lose Control, Stay up to Date BT - Automated Runtime Quality Engineering T2 - Digital Marketplaces Unleashed UR - https://doi.org/10.1007/978-3-662-49275-8_55 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-49275-8_55 SN - 978-3-662-49275-8 SN - 978-3-662-49274-1 SP - 619 EP - 630 PB - Springer CY - Berlin ER - TY - CHAP A1 - Belzner, Lenz A1 - Gabor, Thomas A1 - Wirsing, Martin ED - Steffen, Bernhard T1 - Large Language Model Assisted Software Engineering: Prospects, Challenges, and a Case Study T2 - Bridging the Gap Between AI and Reality: First International Conference, AISoLA 2023, Crete, Greece, October 23–28, 2023, Proceedings UR - https://doi.org/10.1007/978-3-031-46002-9_23 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-46002-9_23 SN - 978-3-031-46002-9 SN - 978-3-031-46001-2 SP - 355 EP - 374 PB - Springer CY - Cham ER - TY - JOUR A1 - González-Alonso, Mónica A1 - Boldeanu, Mihai A1 - Koritnik, Tom A1 - Gonçalves, Jose A1 - Belzner, Lenz A1 - Stemmler, Tom A1 - Gebauer, Robert A1 - Grewling, Łukasz A1 - Tummon, Fiona A1 - Maya-Manzano, Jose M. A1 - Ariño, Arturo H. A1 - Schmidt-Weber, Carsten A1 - Buters, Jeroen T1 - Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors JF - Science of The Total Environment UR - https://doi.org/10.1016/j.scitotenv.2022.160180 Y1 - 2023 UR - https://doi.org/10.1016/j.scitotenv.2022.160180 SN - 1879-1026 SN - 0048-9697 VL - 2023 IS - 861 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Salamat, Babak A1 - Elsbacher, Gerhard A1 - Tonello, Andrea M. A1 - Belzner, Lenz T1 - Model-Free Distributed Reinforcement Learning State Estimation of a Dynamical System Using Integral Value Functions JF - IEEE Open Journal of Control Systems UR - https://doi.org/10.1109/OJCSYS.2023.3250089 Y1 - 2023 UR - https://doi.org/10.1109/OJCSYS.2023.3250089 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-43492 SN - 2694-085X VL - 2 SP - 70 EP - 78 PB - IEEE CY - Piscataway 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 - TY - INPR A1 - Phan, Thomy A1 - Sommer, Felix A1 - Ritz, Fabian A1 - Altmann, Philipp A1 - Nüßlein, Jonas A1 - Kölle, Michael A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning T2 - Research Square N2 - Peer incentivization (PI) is a recent approach, where all agents learn to reward or to penalize each other in a distributed fashion which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly integrated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information which limits scalability and applicability to real-world scenarios, where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to mutually exchange acknowledgment tokens to shape individual rewards. Each agent evaluates the monotonic improvement of its individual situation in order to accept or reject acknowledgment requests from other agents. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can defect from the protocol and where communication failures can occur. We also evaluate the sensitivity of MATE w.r.t. the choice of token values. UR - https://doi.org/10.21203/rs.3.rs-2315844/v1 Y1 - 2022 UR - https://doi.org/10.21203/rs.3.rs-2315844/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-45471 SN - 2693-5015 PB - Research Square CY - Durham ER -