@inproceedings{BhavnaniSchiendorfer2022, author = {Bhavnani, Sidhant and Schiendorfer, Alexander}, title = {Towards copeland optimization in combinatorial problems}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022; Proceedings}, editor = {Schaus, Pierre}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-08010-4}, doi = {https://doi.org/10.1007/978-3-031-08011-1_4}, pages = {34 -- 43}, year = {2022}, language = {en} } @inproceedings{HankeKosakSchiendorferetal.2018, author = {Hanke, Julian and Kosak, Oliver and Schiendorfer, Alexander and Reif, Wolfgang}, title = {Self-organized Resource Allocation for Reconfigurable Robot Ensembles}, booktitle = {2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-5386-5172-8}, doi = {https://doi.org/10.1109/SASO.2018.00022}, pages = {110 -- 119}, year = {2018}, language = {en} } @inproceedings{SchiendorferGajekReif2021, author = {Schiendorfer, Alexander and Gajek, Carola and Reif, Wolfgang}, title = {Turning Software Engineers into Machine Learning Engineers}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, number = {141}, editor = {Bischl, Bernd and Guhr, Oliver and Seibold, Heidi and Steinbach, Peter}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v141/schiendorfer21a.html}, pages = {36 -- 41}, year = {2021}, language = {en} } @inproceedings{EllaGajekSchiendorferetal.2021, author = {Ella, Miriam and Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {An Interactive Web Application for Decision Tree Learning}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, number = {141}, editor = {Bischl, Bernd and Guhr, Oliver and Seibold, Heidi and Steinbach, Peter}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v141/elia21a.html}, pages = {11 -- 16}, year = {2021}, language = {en} } @inproceedings{StieberHoffmannSchiendorferetal.2020, author = {Stieber, Simon and Hoffmann, Alwin and Schiendorfer, Alexander and Reif, Wolfgang and Beyrle, Matthias and Faber, Jan and Richter, Michaela and Sause, Markus}, title = {Towards real-time process monitoring and machine learning for manufacturing composite structures}, booktitle = {Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-8956-7}, doi = {https://doi.org/10.1109/ETFA46521.2020.9212097}, pages = {1455 -- 1458}, year = {2020}, language = {en} } @inproceedings{SchiendorferReif2019, author = {Schiendorfer, Alexander and Reif, Wolfgang}, title = {Reducing bias in preference aggregation for multiagent soft constraint problems}, booktitle = {Principles and Practice of Constraint Programming, 25th International Conference, CP 2019, Proceedings}, editor = {Schiex, Thomas and de Givry, Simon}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-30047-0}, doi = {https://doi.org/10.1007/978-3-030-30048-7_30}, pages = {510 -- 526}, year = {2019}, language = {en} } @inproceedings{GajekSchiendorferReif2019, author = {Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {A chained neural network model for photovoltaic power forecast}, booktitle = {Machine Learning, Optimization, and Data Science: 5th International Conference, LOD 2019, Siena, Italy, September 10-13, 2019, Proceedings}, editor = {Nicosia, Giuseppe and Pardalos, Panos and Umeton, Renato and Giuffrida, Giovanni and Sciacca, Vincenzo}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-37598-0}, issn = {0302-9743}, doi = {https://doi.org/10.1007/978-3-030-37599-7_47}, pages = {566 -- 578}, year = {2019}, language = {en} } @inproceedings{StieberSchroeterSchiendorferetal.2021, author = {Stieber, Simon and Schr{\"o}ter, Niklas and Schiendorfer, Alexander and Hoffmann, Alwin and Reif, Wolfgang}, title = {FlowFrontNet: Improving Carbon Composite Manufacturing with CNNs}, booktitle = {Machine Learning and Knowledge Discovery in Databases, Applied Data Science Track, Proceedings, Part IV}, editor = {Dong, Yuxiao and Mladenić, Dunja and Saunders, Craig}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-67667-4}, doi = {https://doi.org/10.1007/978-3-030-67667-4_25}, pages = {411 -- 426}, year = {2021}, language = {en} } @article{StieberSchroeterFausteretal.2022, author = {Stieber, Simon and Schr{\"o}ter, Niklas and Fauster, Ewald and Bender, Marcel and Schiendorfer, Alexander and Reif, Wolfgang}, title = {Inferring material properties from FRP processes via sim-to-real learning}, volume = {128}, journal = {The International Journal of Advanced Manufacturing Technology}, number = {3-4}, publisher = {Springer}, address = {London}, issn = {1433-3015}, doi = {https://doi.org/10.1007/s00170-023-11509-8}, pages = {1517 -- 1533}, year = {2022}, abstract = {Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 \%, obtained by pretrained transformer models.}, language = {en} } @inproceedings{GajekSchiendorferReif2023, author = {Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks}, booktitle = {Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I}, editor = {Amini, Massih-Reza and Canu, St{\´e}phane and Fischer, Asja and Guns, Tias and Kralj Novak, Petra and Tsoumakas, Grigorios}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-26387-3}, doi = {https://doi.org/10.1007/978-3-031-26387-3_28}, pages = {457 -- 473}, year = {2023}, language = {en} } @inproceedings{LodesSchiendorfer2022, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks}, booktitle = {Proceedings of the European Conference of the PHM Society 2022}, editor = {Do, Phuc and Michau, Gabriel and Ezhilarasu, Cordelia}, publisher = {PHM Society}, address = {State College}, isbn = {978-1-936263-36-3}, doi = {https://doi.org/10.36001/phme.2022.v7i1.3331}, pages = {294 -- 305}, year = {2022}, abstract = {Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100\%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting.}, language = {en} } @inproceedings{RuttmannSchiendorfer2024, author = {Ruttmann, Julia and Schiendorfer, Alexander}, title = {SocialCOP: Reusable Building Blocks for Collective Constraint Optimization}, booktitle = {KI 2024: Advances in Artificial Intelligence, 47th German Conference on AI W{\"u}rzburg, Germany, September 25-27, 2024 Proceedings}, editor = {Hotho, Andreas and Rudolph, Sebastian}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-70893-0}, doi = {https://doi.org/10.1007/978-3-031-70893-0_15}, pages = {204 -- 217}, year = {2024}, language = {en} } @inproceedings{Schiendorfer2015, author = {Schiendorfer, Alexander}, title = {A Soft Constraint Approach to Self-organising Systems}, booktitle = {Organic Computing, Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, Sven and Sick, Bernhard}, publisher = {kassel university press}, address = {Kassel}, isbn = {978-3-7376-0029-3}, url = {https://www.uni-kassel.de/ub/publizieren/kassel-university-press/verlagsprogramm?h=9783737600286}, pages = {3 -- 11}, year = {2015}, language = {en} } @inproceedings{Schiendorfer2014, author = {Schiendorfer, Alexander}, title = {Constraint Programming for Hierarchical Resource Allocation}, booktitle = {Organic Computing, Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, Sven and Sick, Bernhard}, publisher = {kassel university press}, address = {Kassel}, isbn = {978-3-86219-832-0}, url = {https://www.uni-kassel.de/ub/publizieren/kassel-university-press/verlagsprogramm/?h=978-3-86219-832-0}, pages = {57 -- 68}, year = {2014}, language = {en} } @inproceedings{SchiendorferLassnerAndersetal.2015, author = {Schiendorfer, Alexander and Lassner, Christoph and Anders, Gerrit and Reif, Wolfgang and Lienhart, Rainer}, title = {Active Learning for Abstract Models of Collectives}, booktitle = {ARCS 2015 - 28th International Conference on Architecture of Computing Systems, Workshop Proceedings}, editor = {Cardoso, Jo{\~a}o M. P.}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-3657-7}, url = {https://www.vde-verlag.de/proceedings-de/563657010.html}, year = {2015}, language = {en} } @article{AugustineSchwabKlarmannetal.2024, author = {Augustine, Nitin and Schwab, Maximilian and Klarmann, Steffen and Pfefferer, Christian and Schiendorfer, Alexander}, title = {Impact of Blemish Artefacts on Object Detection Models in Autonomous Driving: A Study on Camera Module Manufacturing Defects}, volume = {2024}, journal = {Procedia Computer Science}, number = {232}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2024.01.061}, pages = {616 -- 625}, year = {2024}, abstract = {With the rise of autonomous driving technology, concerns over the safety of AI-based systems have increased, particularly regarding their effect on human life and infrastructure. The complexity and unpredictable nature of AI-enabled software functions have disrupted traditional safety evaluation methods. In such a scenario, the quality control methods used at the manufacturing environments of sensors used for deploying AI-based perception functions need to be coupled with the downstream functions. This study focuses on investigating the impact of a specific production-related defect called blemish, which is caused by debris on image sensors of camera modules. The research aims to answer two fundamental questions: 1) How can blemish artifacts be mathematically modeled, and what is the process for developing a suitable dataset that incorporates this defect? 2) How can the influence of blemish artifacts on performance of object detection models be traced back to define quality requirements of the camera modules? This work takes a novel perspective by shifting the focus from improving model resilience to improving hardware quality camera modules by studying the effects of manufacturing defects on downstream functions. This research seeks to establish a framework to derive quality targets for front camera module manufacturing which can be extended to other sensors for similar applications.}, language = {en} } @inproceedings{LodesSchiendorfer2024, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {A multi-layer machine learning architecture for near real-time inference in manufacturing based on Apache Kafka and selective classification}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Facchinetti, Tullio and Cenedese, Angelo and Lo Bello, Lucia and Vitturi, Stefano and Sauter, Thilo and Tramarin, Federico}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6123-0}, doi = {https://doi.org/10.1109/ETFA61755.2024.10711030}, year = {2024}, language = {en} } @inproceedings{SchiendorferLiscaOutafraoutetal.2024, author = {Schiendorfer, Alexander and Lisca, Gheorghe and Outafraout, Karima and Michailov, Lilia and K{\"a}tzel, Pascal and Felix, Rudolf}, title = {Gas Grid Copilot: Can a MORL Agent Assist a Dispatcher in Managing a Gas Grid?}, booktitle = {ECAI 2024: 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Proceedings}, editor = {Endriss, Ulle and Melo, Francisco S. and Bach, Kerstin and Bugar{\´i}n-Diz, Alberto and Alonso-Moral, Jos{\´e} M. and Barro, Sen{\´e}n and Heintz, Fredrik}, publisher = {IOS Press}, address = {Amsterdam}, isbn = {978-1-64368-548-9}, doi = {https://doi.org/10.3233/FAIA241025}, pages = {4443 -- 4446}, year = {2024}, abstract = {The distribution of fuel gases is undergoing major changes due to decarbonization efforts: Non-fossil gases such as biomethane or renewable hydrogen can lead to the reuse of existing gas infrastructure for gas storage, transport, and distribution to reduce greenhouse gas emissions while maintaining a high energy security. For safe and efficient operation, we propose Gas Grid Copilot (GGC) as a demonstrator of a multi-objective reinforcement learning agent that trains in a simulated gas grid environment to control a grid by modifying its inflow into a mass storage. Multiple, possibly conflicting reward signals are included. Their conflicts and synergies of rewards are analyzed using techniques from multi-criteria decision making, more specifically a conflict interaction matrix based on extended fuzzy logic. That way, dispatchers of a gas grid can explore the effects of reward prioritizations and their consequences safely.}, language = {en} } @inproceedings{SteffelBockSchiendorfer2025, author = {Steffel, Pauline and Bock, J{\"u}rgen and Schiendorfer, Alexander}, title = {Towards a Configurable and Reusable RL Training Infrastructure for AMRs in ROS2}, booktitle = {2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Almeida, Luis and Indria, Marina and de Sousa, Mario and Visioli, Antonio and Ashjaei, Mohammad and Santos, Pedro}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-5383-8}, doi = {https://doi.org/10.1109/ETFA65518.2025.11205702}, year = {2025}, language = {en} } @inproceedings{DachtlerSchiendorfer2025, author = {Dachtler, Kristina and Schiendorfer, Alexander}, title = {A Framework to integrate Machine Learning Decision Preferences in Manufacturing Use Cases}, booktitle = {2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Almeida, Luis and Indria, Marina and de Sousa, Mario and Visioli, Antonio and Ashjaei, Mohammad and Santos, Pedro}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-5383-8}, doi = {https://doi.org/10.1109/ETFA65518.2025.11205573}, year = {2025}, language = {en} } @article{LodesHassDachtleretal.2025, author = {Lodes, Lukas and Hass, Erik and Dachtler, Kristina and Schiendorfer, Alexander}, title = {SmartManPy - Open Source Synthetic Manufacturing Data}, volume = {2025}, journal = {Procedia Computer Science}, number = {253}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2025.01.245}, pages = {1830 -- 1839}, year = {2025}, abstract = {Data Science projects in manufacturing often suffer from difficult access to the necessary high-quality data, such as sensor readings or product quality records. In addition, strict rules due to data protection and intellectual property concerns hinder broad use of the provided data for innovative research, especially in the academic context. To enable the research and evaluation of approaches for a variety of Industry 4.0 use cases, we developed SmartManPy, an extension to the ManPy discrete event simulation environment. We extended the scope of the project to meet our needs and are now able to simulate production lines and the resulting data in just a few lines of code. SmartManPy (Smart Manufacturing Python) includes complex synthetic feature generation, quality control and failures based on recorded features, and data drift simulation. We additionally added a basic environment to train AI agents using reinforcement learning. Our code is released to the public on GitHub.}, language = {en} } @inproceedings{DachtlerOrtnerFerrietal.2023, author = {Dachtler, Kristina and Ortner, Michael and Ferri, Massimo and Eberst, Christof and Schiendorfer, Alexander}, title = {Data-centric and Goal-oriented AI for Robotic Repair Tasks}, booktitle = {ISR Europe 2023, 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 - 27, 2023 in Stuttgart}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-6141-8}, year = {2023}, language = {en} }