@inproceedings{RitzRatkePhanetal.2021, author = {Ritz, Fabian and Ratke, Daniel and Phan, Thomy and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {A sustainable ecosystem through emergent cooperation in multi-agent reinforcement learning}, volume = {2021}, pages = {isal_a_00399}, booktitle = {Proceedings of the Artificial Life Conference 2021}, publisher = {MIT Press}, address = {Cambridge}, doi = {https://doi.org/10.1162/isal_a_00399}, year = {2021}, language = {en} } @article{WaschneckReichstallerBelzneretal.2018, author = {Waschneck, Bernd and Reichstaller, Andr{\´e} and Belzner, Lenz and Altenm{\"u}ller, Thomas and Bauernhansl, Thomas and Knapp, Alexander and Kyek, Andreas}, title = {Optimization of global production scheduling with deep reinforcement learning}, volume = {2018}, journal = {Procedia CIRP}, number = {72}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {https://doi.org/10.1016/j.procir.2018.03.212}, pages = {1264 -- 1269}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{SchmidBelznerLinnhoffPopien2021, author = {Schmid, Kyrill and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Learning to penalize other learning agents}, volume = {2021}, pages = {isal_a_00369}, booktitle = {Proceedings of the Artificial Life Conference 2021}, publisher = {MIT Press}, address = {Cambridge}, doi = {https://doi.org/10.1162/isal_a_00369}, year = {2021}, language = {en} } @inproceedings{PhanRitzBelzneretal.2021, author = {Phan, Thomy and Ritz, Fabian and Belzner, Lenz and Altmann, Philipp and Gabor, Thomas and Linnhoff-Popien, Claudia}, title = {VAST: Value Function Factorization with Variable Agent Sub-Teams}, booktitle = {Advances in Neural Information Processing Systems 34 (NeurIPS 2021)}, publisher = {Neural Information Processing Systems Foundation, Inc. (NIPS)}, url = {https://proceedings.neurips.cc/paper/2021/hash/c97e7a5153badb6576d8939469f58336-Abstract.html}, year = {2021}, language = {en} } @inproceedings{BelznerSchmidPhanetal.2018, author = {Belzner, Lenz and Schmid, Kyrill and Phan, Thomy and Gabor, Thomas and Wirsing, Martin}, title = {The sharer's dilemma in collective adaptive systems of self-interested agents}, booktitle = {Leveraging Applications of Formal Methods, Verification and Validation : Distributed Systems : 8th International Symposium, ISoLA 2018; Limassol, Cyprus, November 5-9, 2018; Proceedings, Part III}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-03423-8}, doi = {https://doi.org/10.1007/978-3-030-03424-5_16}, pages = {241 -- 256}, year = {2018}, language = {en} } @inproceedings{UnoldWintergerstBelzneretal.2021, author = {Unold, Florian von and Wintergerst, Monika and Belzner, Lenz and Groh, Georg}, title = {DYME: a dynamic metric for dialog modeling learned from human conversations}, booktitle = {Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8-12, 2021, Proceedings, Part V}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-92307-5}, doi = {https://doi.org/10.1007/978-3-030-92307-5_30}, pages = {257 -- 264}, year = {2021}, language = {en} } @inproceedings{SchmidMuellerBelzneretal.2021, author = {Schmid, Kyrill and M{\"u}ller, Robert and Belzner, Lenz and Tochtermann, Johannes and Linnhoff-Popien, Claudia}, title = {Distributed emergent agreements with deep reinforcement learning}, booktitle = {2021 International Joint Conference on Neural Networks (IJCNN) Proceedings}, publisher = {IEEE}, isbn = {978-1-6654-3900-8}, issn = {2161-4407}, doi = {https://doi.org/10.1109/IJCNN52387.2021.9533333}, year = {2021}, language = {en} } @article{GaborIlliumZornetal.2022, author = {Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Self-Replication in Neural Networks}, volume = {28}, journal = {Artificial Life}, number = {2}, publisher = {MIT Press}, address = {Cambridge}, issn = {1530-9185}, doi = {https://doi.org/10.1162/artl_a_00359}, pages = {205 -- 223}, year = {2022}, language = {en} } @article{GaborSedlmeierPhanetal.2020, author = {Gabor, Thomas and Sedlmeier, Andreas and Phan, Thomy and Ritz, Fabian and Kiermeier, Marie and Belzner, Lenz and Kempter, Bernhard and Klein, Cornel and Sauer, Horst and Schmid, Reiner and Wieghardt, Jan and Zeller, Marc and Linnhoff-Popien, Claudia}, title = {The scenario coevolution paradigm}, volume = {22}, journal = {International Journal on Software Tools for Technology Transfer}, subtitle = {adaptive quality assurance for adaptive systems}, number = {4}, publisher = {Springer}, address = {Berlin}, issn = {1433-2787}, doi = {https://doi.org/10.1007/s10009-020-00560-5}, pages = {457 -- 476}, year = {2020}, abstract = {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).}, language = {en} } @inproceedings{SchmidBelznerPhanetal.2020, author = {Schmid, Kyrill and Belzner, Lenz and Phan, Thomy and Gabor, Thomas and Linnhoff-Popien, Claudia}, title = {Multi-agent reinforcement learning for bargaining under risk and asymmetric information}, booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART 2020}, editor = {Rocha, Ana and Steels, Luc and Herik, Jaap van den}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-395-7}, issn = {2184-433X}, doi = {https://doi.org/10.5220/0008913901440151}, pages = {144 -- 151}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{SedlmeierGaborPhanetal.2020, author = {Sedlmeier, Andreas and Gabor, Thomas and Phan, Thomy and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Uncertainty-based out-of-distribution classification in deep reinforcement learning}, booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART}, editor = {Rocha, Ana and Steels, Luc and Herik, Jaap van den}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-395-7}, issn = {2184-433X}, doi = {https://doi.org/10.5220/0008949905220529}, pages = {522 -- 529}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{GaborSuenkelRitzetal.2020, author = {Gabor, Thomas and S{\"u}nkel, Leo and Ritz, Fabian and Phan, Thomy and Belzner, Lenz and Roch, Christoph and Feld, Sebastian and Linnhoff-Popien, Claudia}, title = {The holy grail of quantum artificial intelligence}, booktitle = {ICSEW'20 : Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops}, subtitle = {major challenges in accelerating the machine learning pipeline}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-7963-2}, doi = {https://doi.org/10.1145/3387940.3391469}, pages = {456 -- 461}, year = {2020}, language = {en} } @inproceedings{PhanBelznerGaboretal.2021, author = {Phan, Thomy and Belzner, Lenz and Gabor, Thomas and Sedlmeier, Andreas and Ritz, Fabian and Linnhoff-Popien, Claudia}, title = {Resilient multi-agent reinforcement learning with adversarial value decomposition}, volume = {35}, booktitle = {AAAI-21 / IAAI-21 / EAAI-21 Proceedings}, number = {13}, publisher = {AAAI Press}, address = {Palo Alto (CA)}, isbn = {978-1-57735-866-4}, issn = {2374-3468}, doi = {https://ojs.aaai.org/index.php/AAAI/article/view/17348}, pages = {11308 -- 11316}, year = {2021}, language = {en} } @inproceedings{FeldSedlmeierFriedrichetal.2019, author = {Feld, Sebastian and Sedlmeier, Andreas and Friedrich, Markus and Franz, Jan and Belzner, Lenz}, title = {Bayesian Surprise in Indoor Environments}, booktitle = {SIGSPATIAL '19 : Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-6909-1}, doi = {https://doi.org/10.1145/3347146.3359358}, year = {2019}, language = {en} } @inproceedings{PhanBelznerKiermeieretal.2019, author = {Phan, Thomy and Belzner, Lenz and Kiermeier, Marie and Friedrich, Markus and Schmid, Kyrill and Linnhoff-Popien, Claudia}, title = {Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling}, volume = {33}, booktitle = {AAAI-19 / IAAI-19 / EAAI-20 Proceedings}, number = {1}, publisher = {AAAI Press}, address = {Palo Alto}, issn = {2374-3468}, doi = {https://doi.org/10.1609/aaai.v33i01.33017941}, pages = {7941 -- 7948}, year = {2019}, language = {en} } @article{PosorBelznerKnapp2019, author = {Posor, Jorrit Enzio and Belzner, Lenz and Knapp, Alexander}, title = {Joint Action Learning for Multi-Agent Cooperation using Recurrent Reinforcement Learning}, volume = {4}, journal = {Digitale Welt}, number = {1}, publisher = {Digitale Welt Academy}, address = {M{\"u}nchen}, issn = {2569-1996}, doi = {https://doi.org/10.1007/s42354-019-0239-y}, pages = {79 -- 84}, year = {2019}, language = {en} } @inproceedings{PierothFitchBelzner2022, author = {Pieroth, Fabian Raoul and Fitch, Katherine and Belzner, Lenz}, title = {Detecting Influence Structures in Multi-Agent Reinforcement Learning Systems}, url = {http://aaai-rlg.mlanctot.info/sched.html\#poster1}, year = {2022}, language = {en} } @article{BelznerWirsing2021, author = {Belzner, Lenz and Wirsing, Martin}, title = {Synthesizing safe policies under probabilistic constraints with reinforcement learning and Bayesian model checking}, volume = {2021}, pages = {102620}, journal = {Science of Computer Programming}, number = {206}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-6423}, doi = {https://doi.org/10.1016/j.scico.2021.102620}, year = {2021}, language = {en} } @inproceedings{PhanSchmidBelzneretal.2019, author = {Phan, Thomy and Schmid, Kyrill and Belzner, Lenz and Gabor, Thomas and Feld, Sebastian and Linnhoff-Popien, Claudia}, title = {Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies}, booktitle = {AAMAS '19 : Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland}, isbn = {978-1-4503-6309-9}, url = {https://www.ifaamas.org/Proceedings/aamas2019/forms/contents.htm}, pages = {2162 -- 2164}, year = {2019}, language = {en} } @inproceedings{SchmidBelznerKiermeieretal.2018, author = {Schmid, Kyrill and Belzner, Lenz and Kiermeier, Marie and Neitz, Alexander and Phan, Thomy and Gabor, Thomas and Linnhoff-Popien, Claudia}, title = {Risk-sensitivity in simulation based online planning}, booktitle = {KI 2018: Advances in Artificial Intelligence : Proceedings}, editor = {Trollmann, Frank and Turhan, Anni-Yasmin}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-00111-7}, doi = {https://doi.org/10.1007/978-3-030-00111-7_20}, pages = {229 -- 240}, year = {2018}, language = {en} } @inproceedings{GaborBelznerLinnhoffPopien2018, author = {Gabor, Thomas and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Inheritance-based diversity measures for explicit convergence control in evolutionary algorithms}, booktitle = {GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference}, editor = {Aguirre, Hernan}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5618-3}, doi = {https: //doi.org/10.1145/3205455.3205630}, pages = {841 -- 848}, year = {2018}, language = {en} } @inproceedings{FeldSedlmeierIlliumetal.2018, author = {Feld, Sebastian and Sedlmeier, Andreas and Illium, Steffen and Belzner, Lenz}, title = {Trajectory annotation using sequences of spatial perception}, booktitle = {SIGSPATIAL '18 : Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5889-7}, doi = {https://doi.org/10.1145/3274895.3274968}, pages = {329 -- 338}, year = {2018}, language = {en} } @inproceedings{GaborBelznerPhanetal.2018, author = {Gabor, Thomas and Belzner, Lenz and Phan, Thomy and Schmid, Kyrill}, title = {Preparing for the unexpected}, booktitle = {Proceedings : 15th IEEE International Conference on Autonomic Computing - ICAC 2018}, subtitle = {diversity improves planning resilience in evolutionary algorithms}, publisher = {IEEE}, address = {Los Alamitos (CA)}, isbn = {978-1-5386-5139-1}, issn = {2474-0756}, doi = {https://doi.org/10.1109/ICAC.2018.00023}, pages = {131 -- 140}, year = {2018}, language = {en} } @inproceedings{SchmidBelznerGaboretal.2018, author = {Schmid, Kyrill and Belzner, Lenz and Gabor, Thomas and Phan, Thomy}, title = {Action markets in deep multi-agent reinforcement learning}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2018 : Proceedings, Part II}, editor = {Kurkova, Vera and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-01421-6}, doi = {https://doi.org/10.1007/978-3-030-01421-6_24}, pages = {240 -- 249}, year = {2018}, language = {en} } @inproceedings{HahnPhanGaboretal.2019, author = {Hahn, Carsten and Phan, Thomy and Gabor, Thomas and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Emergent Escape-based Flocking behavior using Multi-Agent Reinforcement Learning}, pages = {isal_a_00226}, booktitle = {Artificial Life Conference Proceedings}, publisher = {MIT Press}, address = {Cambridge (MA)}, doi = {https://doi.org/10.1162/isal_a_00226}, pages = {598 -- 605}, year = {2019}, language = {en} } @article{SedlmeierGaborPhanetal.2019, author = {Sedlmeier, Andreas and Gabor, Thomas and Phan, Thomy and Belzner, Lenz}, title = {Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning}, volume = {4}, journal = {Digitale Welt}, number = {1}, publisher = {Digitale Welt Academy}, address = {M{\"u}nchen}, issn = {2569-1996}, doi = {https://doi.org/10.1007/s42354-019-0238-z}, pages = {74 -- 78}, year = {2019}, language = {en} } @inproceedings{GaborIlliumMattauschetal.2019, author = {Gabor, Thomas and Illium, Steffen and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Self-Replication in Neural Networks}, pages = {isal_a_00197}, booktitle = {Artificial Life Conference Proceedings}, publisher = {MIT Press}, address = {Cambridge}, doi = {https://doi.org/10.1162/isal_a_00197}, pages = {424 -- 431}, year = {2019}, language = {en} } @inbook{WirsingBelzner2023, author = {Wirsing, Martin and Belzner, Lenz}, title = {Towards Systematically Engineering Autonomous Systems Using Reinforcement Learning and Planning}, booktitle = {Analysis, Verification and Transformation for Declarative Programming and Intelligent Systems}, editor = {Lopez-Garcia, Pedro and Gallagher, John P. and Giacobazzi, Roberto}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-31476-6}, doi = {https://doi.org/10.1007/978-3-031-31476-6_16}, pages = {281 -- 306}, year = {2023}, language = {en} } @inproceedings{SunPierothSchmidetal.2022, author = {Sun, Xiyue and Pieroth, Fabian Raoul and Schmid, Kyrill and Wirsing, Martin and Belzner, Lenz}, title = {On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives}, booktitle = {Proceedings: 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops: ICDCSW 2022}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-6654-8879-2}, issn = {2332-5666}, doi = {https://doi.org/10.1109/ICDCSW56584.2022.00031}, pages = {113 -- 118}, year = {2022}, language = {en} } @inproceedings{PhanSommerAltmannetal.2022, author = {Phan, Thomy and Sommer, Felix and Altmann, Philipp and Ritz, Fabian and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Emergent Cooperation from Mutual Acknowledgment Exchange}, booktitle = {Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022)}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland}, isbn = {978-1-4503-9213-6}, url = {https://dl.acm.org/doi/10.5555/3535850.3535967}, pages = {1047 -- 1055}, year = {2022}, language = {en} } @inproceedings{WaschneckReichstallerBelzneretal.2018, author = {Waschneck, Bernd and Reichstaller, Andr{\´e} and Belzner, Lenz and Altenm{\"u}ller, Thomas and Bauernhansl, Thomas and Knapp, Alexander and Kyek, Andreas}, title = {Deep reinforcement learning for semiconductor production scheduling}, booktitle = {2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-3748-7}, issn = {2376-6697}, doi = {https://doi.org/10.1109/ASMC.2018.8373191}, pages = {301 -- 306}, year = {2018}, language = {en} } @inproceedings{PhanBelznerGaboretal.2018, author = {Phan, Thomy and Belzner, Lenz and Gabor, Thomas and Schmid, Kyrill}, title = {Leveraging statistical multi-agent online planning with emergent value function approximation}, booktitle = {AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems}, publisher = {International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS)}, address = {Richland}, isbn = {978-1-4503-5649-7}, issn = {2523-5699}, url = {https://www.ifaamas.org/Proceedings/aamas2018/forms/contents.htm\#18}, pages = {730 -- 738}, year = {2018}, language = {en} } @inbook{GaborKiermeierBelzner2018, author = {Gabor, Thomas and Kiermeier, Marie and Belzner, Lenz}, title = {Don't Lose Control, Stay up to Date}, booktitle = {Digital Marketplaces Unleashed}, subtitle = {Automated Runtime Quality Engineering}, editor = {Linnhoff-Popien, Claudia and Schneider, Ralf and Zaddach, Michael}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-662-49275-8}, doi = {https://doi.org/10.1007/978-3-662-49275-8_55}, pages = {619 -- 630}, year = {2018}, language = {en} } @inproceedings{BelznerGaborWirsing2023, author = {Belzner, Lenz and Gabor, Thomas and Wirsing, Martin}, title = {Large Language Model Assisted Software Engineering: Prospects, Challenges, and a Case Study}, booktitle = {Bridging the Gap Between AI and Reality: First International Conference, AISoLA 2023, Crete, Greece, October 23-28, 2023, Proceedings}, editor = {Steffen, Bernhard}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-46002-9}, doi = {https://doi.org/10.1007/978-3-031-46002-9_23}, pages = {355 -- 374}, year = {2023}, language = {en} } @article{GonzalezAlonsoBoldeanuKoritniketal.2023, author = {Gonz{\´a}lez-Alonso, M{\´o}nica and Boldeanu, Mihai and Koritnik, Tom and Gon{\c{c}}alves, Jose and Belzner, Lenz and Stemmler, Tom and Gebauer, Robert and Grewling, Łukasz and Tummon, Fiona and Maya-Manzano, Jose M. and Ari{\~n}o, Arturo H. and Schmidt-Weber, Carsten and Buters, Jeroen}, title = {Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors}, volume = {2023}, pages = {160180}, journal = {Science of The Total Environment}, number = {861}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1879-1026}, doi = {https://doi.org/10.1016/j.scitotenv.2022.160180}, year = {2023}, language = {en} } @article{SalamatElsbacherTonelloetal.2023, author = {Salamat, Babak and Elsbacher, Gerhard and Tonello, Andrea M. and Belzner, Lenz}, title = {Model-Free Distributed Reinforcement Learning State Estimation of a Dynamical System Using Integral Value Functions}, volume = {2}, journal = {IEEE Open Journal of Control Systems}, publisher = {IEEE}, address = {Piscataway}, issn = {2694-085X}, doi = {https://doi.org/10.1109/OJCSYS.2023.3250089}, pages = {70 -- 78}, year = {2023}, language = {en} } @inproceedings{SchmidBelznerMuelleretal.2021, author = {Schmid, Kyrill and Belzner, Lenz and M{\"u}ller, Robert and Tochtermann, Johannes and Linnhoff-Popien, Claudia}, title = {Stochastic market games}, booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence}, publisher = {IJCAI}, address = {[s. l.]}, isbn = {978-0-9992411-9-6}, doi = {https://doi.org/10.24963/ijcai.2021/54}, pages = {384 -- 390}, year = {2021}, language = {en} } @unpublished{PhanSommerRitzetal.2022, author = {Phan, Thomy and Sommer, Felix and Ritz, Fabian and Altmann, Philipp and N{\"u}ßlein, Jonas and K{\"o}lle, Michael and Belzner, Lenz and Linnhoff-Popien, Claudia}, title = {Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-2315844/v1}, year = {2022}, abstract = {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.}, language = {en} }