@inproceedings{SchiendorferSteghoeferKnappetal.2013, author = {Schiendorfer, Alexander and Stegh{\"o}fer, Jan-Philipp and Knapp, Alexander and Nafz, Florian and Reif, Wolfgang}, title = {Constraint Relationships for Soft Constraints}, booktitle = {Research and Development in Intelligent Systems XXX: Incorporating Applications and Innovations in Intelligent Systems XXI: Proceedings of AI-2013}, editor = {Bramer, Max and Petridis, Miltos}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-02621-3}, doi = {https://doi.org/10.1007/978-3-319-02621-3_17}, pages = {241 -- 255}, year = {2013}, language = {en} } @inproceedings{KnappSchiendorferReif2014, author = {Knapp, Alexander and Schiendorfer, Alexander and Reif, Wolfgang}, title = {Quality over Quantity in Soft Constraints}, booktitle = {Proceedings: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence: ICTAI 2014}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-4799-6572-4}, doi = {https://doi.org/10.1109/ICTAI.2014.75}, pages = {453 -- 460}, year = {2014}, language = {en} } @inbook{SchiendorferKnappSteghoeferetal.2015, author = {Schiendorfer, Alexander and Knapp, Alexander and Stegh{\"o}fer, Jan-Philipp and Anders, Gerrit and Siefert, Florian and Reif, Wolfgang}, title = {Partial Valuation Structures for Qualitative Soft Constraints}, booktitle = {Software, Services, and Systems: Essays Dedicated to MartinWirsing on the Occasion of His Retirement from the Chair of Programming and Software Engineering}, editor = {De Nicola, Rocco and Hennicker, Rolf}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-15545-6}, doi = {https://doi.org/10.1007/978-3-319-15545-6_10}, pages = {115 -- 133}, year = {2015}, language = {en} } @article{SchiendorferKnappAndersetal.2018, author = {Schiendorfer, Alexander and Knapp, Alexander and Anders, Gerrit and Reif, Wolfgang}, title = {MiniBrass: Soft constraints for MiniZinc}, volume = {23}, journal = {Constraints}, number = {4}, publisher = {Springer}, address = {Dodrecht}, issn = {1572-9354}, doi = {https://doi.org/10.1007/s10601-018-9289-2}, pages = {403 -- 450}, year = {2018}, 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} } @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{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} }