[{"department":[{"_id":"195"}],"keyword":["Reputation Systems","Rating systems","monetary ratings","incentive mechanism","systems theory","Market coordination","advanced review system"],"type":"dissertation","place":"Paderborn","date_created":"2026-04-02T03:52:09Z","supervisor":[{"last_name":"Beverungen","first_name":"Daniel","full_name":"Beverungen, Daniel"},{"first_name":"Dennis","last_name":"Kundisch","full_name":"Kundisch, Dennis"}],"citation":{"short":"S. Hemmrich, A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets, Universität Paderborn, Paderborn, 2025.","chicago":"Hemmrich, Simon. <i>A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets</i>. Paderborn: Universität Paderborn, 2025. <a href=\"https://doi.org/10.17619/UNIPB/1-2414\">https://doi.org/10.17619/UNIPB/1-2414</a>.","apa":"Hemmrich, S. (2025). <i>A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets</i>. Universität Paderborn. <a href=\"https://doi.org/10.17619/UNIPB/1-2414\">https://doi.org/10.17619/UNIPB/1-2414</a>","ieee":"S. Hemmrich, <i>A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets</i>. Paderborn: Universität Paderborn, 2025.","ama":"Hemmrich S. <i>A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets</i>. Universität Paderborn; 2025. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2414\">https://doi.org/10.17619/UNIPB/1-2414</a>","bibtex":"@book{Hemmrich_2025, place={Paderborn}, title={A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-2414\">https://doi.org/10.17619/UNIPB/1-2414</a>}, publisher={Universität Paderborn}, author={Hemmrich, Simon}, year={2025} }","mla":"Hemmrich, Simon. <i>A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets</i>. Universität Paderborn, 2025, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2414\">https://doi.org/10.17619/UNIPB/1-2414</a>."},"doi":"https://doi.org/10.17619/UNIPB/1-2414","user_id":"83557","_id":"65309","language":[{"iso":"eng"}],"publisher":"Universität Paderborn","page":"347","date_updated":"2026-04-02T04:31:57Z","publication_status":"published","jel":["D8"],"author":[{"full_name":"Hemmrich, Simon","last_name":"Hemmrich","first_name":"Simon","id":"83557"}],"title":"A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets","status":"public","year":"2025"},{"date_updated":"2026-02-17T16:15:41Z","intvolume":"       432","title":"Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane","status":"public","year":"2024","author":[{"full_name":"Kunkel, Benny","first_name":"Benny","last_name":"Kunkel"},{"last_name":"Seeburg","first_name":"Dominik","full_name":"Seeburg, Dominik"},{"last_name":"Kabelitz","first_name":"Anke","full_name":"Kabelitz, Anke"},{"full_name":"Witte, Steffen","last_name":"Witte","first_name":"Steffen"},{"id":"118165","full_name":"Gutmann, Torsten","last_name":"Gutmann","first_name":"Torsten"},{"first_name":"Hergen","last_name":"Breitzke","full_name":"Breitzke, Hergen"},{"full_name":"Buntkowsky, Gerd","last_name":"Buntkowsky","first_name":"Gerd"},{"first_name":"Ana Guilherme","last_name":"Buzanich","full_name":"Buzanich, Ana Guilherme"},{"full_name":"Wohlrab, Sebastian","last_name":"Wohlrab","first_name":"Sebastian"}],"doi":"10.1016/j.cattod.2024.114643","user_id":"100715","volume":432,"page":"114643","language":[{"iso":"eng"}],"_id":"64002","abstract":[{"text":"The production of formaldehyde on industrial scale requires huge amounts of energy due to the involvement of reforming processes in combination with the demand in the megaton scale. Hence, a direct route for the transformation of (bio)methane to formaldehyde would decrease costs and puts less pressure on the environment. Herein, we report on the use of zinc modified silicas as possible support materials for vanadium catalysts and the resulting consequences for the performance in the selective oxidation of methane to formaldehyde. After optimization of the Zn content and reaction conditions, a remarkably high space-time yield of 12.4 kgCH2O·kgcat−1·h−1 was achieved. As a result of the extensive characterization by means of UV–vis, Raman, XANES and NMR spectroscopy it was found that vanadium is in the vicinity of highly dispersed zinc atoms which promote the formation of active vanadium species as supposed by theoretical calculations. This work presents a further step of catalyst development towards direct industrial methane conversion which may help to overcome current limitations in the future.","lang":"eng"}],"extern":"1","publication":"Catalysis Today","citation":{"short":"B. Kunkel, D. Seeburg, A. Kabelitz, S. Witte, T. Gutmann, H. Breitzke, G. Buntkowsky, A.G. Buzanich, S. Wohlrab, Catalysis Today 432 (2024) 114643.","chicago":"Kunkel, Benny, Dominik Seeburg, Anke Kabelitz, Steffen Witte, Torsten Gutmann, Hergen Breitzke, Gerd Buntkowsky, Ana Guilherme Buzanich, and Sebastian Wohlrab. “Highly Productive V/Zn-SiO2 Catalysts for the Selective Oxidation of Methane.” <i>Catalysis Today</i> 432 (2024): 114643. <a href=\"https://doi.org/10.1016/j.cattod.2024.114643\">https://doi.org/10.1016/j.cattod.2024.114643</a>.","ieee":"B. Kunkel <i>et al.</i>, “Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane,” <i>Catalysis Today</i>, vol. 432, p. 114643, 2024, doi: <a href=\"https://doi.org/10.1016/j.cattod.2024.114643\">10.1016/j.cattod.2024.114643</a>.","apa":"Kunkel, B., Seeburg, D., Kabelitz, A., Witte, S., Gutmann, T., Breitzke, H., Buntkowsky, G., Buzanich, A. G., &#38; Wohlrab, S. (2024). Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane. <i>Catalysis Today</i>, <i>432</i>, 114643. <a href=\"https://doi.org/10.1016/j.cattod.2024.114643\">https://doi.org/10.1016/j.cattod.2024.114643</a>","bibtex":"@article{Kunkel_Seeburg_Kabelitz_Witte_Gutmann_Breitzke_Buntkowsky_Buzanich_Wohlrab_2024, title={Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane}, volume={432}, DOI={<a href=\"https://doi.org/10.1016/j.cattod.2024.114643\">10.1016/j.cattod.2024.114643</a>}, journal={Catalysis Today}, author={Kunkel, Benny and Seeburg, Dominik and Kabelitz, Anke and Witte, Steffen and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky, Gerd and Buzanich, Ana Guilherme and Wohlrab, Sebastian}, year={2024}, pages={114643} }","ama":"Kunkel B, Seeburg D, Kabelitz A, et al. Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane. <i>Catalysis Today</i>. 2024;432:114643. doi:<a href=\"https://doi.org/10.1016/j.cattod.2024.114643\">10.1016/j.cattod.2024.114643</a>","mla":"Kunkel, Benny, et al. “Highly Productive V/Zn-SiO2 Catalysts for the Selective Oxidation of Methane.” <i>Catalysis Today</i>, vol. 432, 2024, p. 114643, doi:<a href=\"https://doi.org/10.1016/j.cattod.2024.114643\">10.1016/j.cattod.2024.114643</a>."},"type":"journal_article","keyword":["Formaldehyde","Local coordination","SBA-15","Vanadium oxo species","XANES","Zinc doped silica"],"date_created":"2026-02-07T15:53:56Z"},{"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"quality_controlled":"1","citation":{"short":"S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.","chicago":"Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker, and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” In <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE, 2022.","ieee":"S. B. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.","apa":"Schneider, S. B., Werner, S., Khalili, R., Hecker, A., &#38; Karl, H. (2022). mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.","bibtex":"@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}, year={2022} }","ama":"Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. In: <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE; 2022.","mla":"Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>, IEEE, 2022."},"file_date_updated":"2022-03-10T18:25:41Z","oa":"1","has_accepted_license":"1","conference":{"end_date":"2022-04-29","name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","start_date":"2022-04-25","location":"Budapest"},"status":"public","ddc":["004"],"user_id":"35343","_id":"30236","publisher":"IEEE","abstract":[{"text":"Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive\r\nresults. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of\r\nwireless mobile networks.","lang":"eng"}],"publication":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","department":[{"_id":"75"}],"type":"conference","keyword":["wireless mobile networks","network management","continuous control","cognitive networks","autonomous coordination","reinforcement learning","gym environment","simulation","open source"],"date_created":"2022-03-10T18:28:14Z","file":[{"creator":"stschn","date_created":"2022-03-10T18:25:41Z","file_name":"author_version.pdf","file_size":223412,"access_level":"open_access","relation":"main_file","date_updated":"2022-03-10T18:25:41Z","file_id":"30237","content_type":"application/pdf"}],"date_updated":"2022-03-10T18:28:19Z","author":[{"last_name":"Schneider","first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343"},{"last_name":"Werner","first_name":"Stefan","full_name":"Werner, Stefan"},{"full_name":"Khalili, Ramin","last_name":"Khalili","first_name":"Ramin"},{"full_name":"Hecker, Artur","last_name":"Hecker","first_name":"Artur"},{"full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger","id":"126"}],"title":"mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks","year":"2022","language":[{"iso":"eng"}]},{"file":[{"title":"Distributed Online Service Coordination Using Deep Reinforcement Learning","file_id":"21544","content_type":"application/pdf","relation":"main_file","date_updated":"2021-03-18T17:12:56Z","file_name":"public_author_version.pdf","access_level":"open_access","file_size":606321,"date_created":"2021-03-18T17:12:56Z","creator":"stschn"}],"date_created":"2021-03-18T17:15:47Z","type":"conference","keyword":["network management","service management","coordination","reinforcement learning","distributed"],"department":[{"_id":"75"}],"publication":"IEEE International Conference on Distributed Computing Systems (ICDCS)","related_material":{"link":[{"relation":"software","url":"https://github.com/ RealVNF/distributed-drl-coordination"}]},"abstract":[{"text":"Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).","lang":"eng"}],"language":[{"iso":"eng"}],"title":"Distributed Online Service Coordination Using Deep Reinforcement Learning","year":"2021","author":[{"full_name":"Schneider, Stefan Balthasar","first_name":"Stefan Balthasar","last_name":"Schneider","orcid":"0000-0001-8210-4011","id":"35343"},{"last_name":"Qarawlus","first_name":"Haydar","full_name":"Qarawlus, Haydar"},{"id":"126","full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger"}],"date_updated":"2022-01-06T06:55:04Z","oa":"1","file_date_updated":"2021-03-18T17:12:56Z","citation":{"apa":"Schneider, S. B., Qarawlus, H., &#38; Karl, H. (2021). Distributed Online Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. Washington, DC, USA: IEEE.","ieee":"S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>, Washington, DC, USA, 2021.","short":"S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021.","chicago":"Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. IEEE, 2021.","mla":"Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>, IEEE, 2021.","ama":"Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. IEEE; 2021.","bibtex":"@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }"},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"publisher":"IEEE","_id":"21543","ddc":["000"],"user_id":"35343","status":"public","conference":{"name":"IEEE International Conference on Distributed Computing Systems (ICDCS)","location":"Washington, DC, USA"},"has_accepted_license":"1"},{"author":[{"id":"35343","first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011","last_name":"Schneider","full_name":"Schneider, Stefan Balthasar"},{"last_name":"Jürgens","first_name":"Mirko","full_name":"Jürgens, Mirko"},{"id":"126","last_name":"Karl","first_name":"Holger","full_name":"Karl, Holger"}],"year":"2021","title":"Divide and Conquer: Hierarchical Network and Service Coordination","date_updated":"2022-01-06T06:54:32Z","language":[{"iso":"eng"}],"publication":"IFIP/IEEE International Symposium on Integrated Network Management (IM)","abstract":[{"lang":"eng","text":"In practical, large-scale networks, services are requested\r\nby users across the globe, e.g., for video streaming.\r\nServices consist of multiple interconnected components such as\r\nmicroservices in a service mesh. Coordinating these services\r\nrequires scaling them according to continuously changing user\r\ndemand, deploying instances at the edge close to their users,\r\nand routing traffic efficiently between users and connected instances.\r\nNetwork and service coordination is commonly addressed\r\nthrough centralized approaches, where a single coordinator\r\nknows everything and coordinates the entire network globally.\r\nWhile such centralized approaches can reach global optima, they\r\ndo not scale to large, realistic networks. In contrast, distributed\r\napproaches scale well, but sacrifice solution quality due to their\r\nlimited scope of knowledge and coordination decisions.\r\n\r\nTo this end, we propose a hierarchical coordination approach\r\nthat combines the good solution quality of centralized approaches\r\nwith the scalability of distributed approaches. In doing so, we divide\r\nthe network into multiple hierarchical domains and optimize\r\ncoordination in a top-down manner. We compare our hierarchical\r\nwith a centralized approach in an extensive evaluation on a real-world\r\nnetwork topology. Our results indicate that hierarchical\r\ncoordination can find close-to-optimal solutions in a fraction of\r\nthe runtime of centralized approaches."}],"date_created":"2020-12-11T08:39:47Z","file":[{"file_name":"preprint_with_header.pdf","access_level":"open_access","file_size":7979772,"relation":"main_file","date_updated":"2020-12-11T08:37:37Z","file_id":"20694","content_type":"application/pdf","title":"Divide and Conquer: Hierarchical Network and Service Coordination","creator":"stschn","date_created":"2020-12-11T08:37:37Z"}],"department":[{"_id":"75"}],"keyword":["network management","service management","coordination","hierarchical","scalability","nfv"],"type":"conference","conference":{"name":"IFIP/IEEE International Symposium on Integrated Network Management (IM)","location":"Bordeaux, France"},"status":"public","has_accepted_license":"1","publisher":"IFIP/IEEE","_id":"20693","ddc":["006"],"user_id":"35343","citation":{"bibtex":"@inproceedings{Schneider_Jürgens_Karl_2021, title={Divide and Conquer: Hierarchical Network and Service Coordination}, booktitle={IFIP/IEEE International Symposium on Integrated Network Management (IM)}, publisher={IFIP/IEEE}, author={Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}, year={2021} }","ama":"Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network and Service Coordination. In: <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE; 2021.","mla":"Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network and Service Coordination.” <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>, IFIP/IEEE, 2021.","chicago":"Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and Conquer: Hierarchical Network and Service Coordination.” In <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE, 2021.","short":"S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.","ieee":"S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical Network and Service Coordination,” in <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>, Bordeaux, France, 2021.","apa":"Schneider, S. B., Jürgens, M., &#38; Karl, H. (2021). Divide and Conquer: Hierarchical Network and Service Coordination. In <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. Bordeaux, France: IFIP/IEEE."},"file_date_updated":"2020-12-11T08:37:37Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - Project Area C"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"quality_controlled":"1","oa":"1"},{"title":"Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning","year":"2021","author":[{"id":"35343","orcid":"0000-0001-8210-4011","first_name":"Stefan Balthasar","last_name":"Schneider","full_name":"Schneider, Stefan Balthasar"},{"last_name":"Khalili","first_name":"Ramin","full_name":"Khalili, Ramin"},{"last_name":"Manzoor","first_name":"Adnan","full_name":"Manzoor, Adnan"},{"full_name":"Qarawlus, Haydar","last_name":"Qarawlus","first_name":"Haydar"},{"full_name":"Schellenberg, Rafael","last_name":"Schellenberg","first_name":"Rafael"},{"id":"126","full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger"},{"first_name":"Artur","last_name":"Hecker","full_name":"Hecker, Artur"}],"date_updated":"2022-01-06T06:55:15Z","article_type":"original","language":[{"iso":"eng"}],"doi":"10.1109/TNSM.2021.3076503","publication":"Transactions on Network and Service Management","abstract":[{"text":"Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.","lang":"eng"}],"file":[{"description":"Author version of the accepted paper","date_created":"2021-04-27T08:01:26Z","creator":"stschn","file_id":"21809","content_type":"application/pdf","relation":"main_file","date_updated":"2021-04-27T08:01:26Z","file_name":"ris-accepted-version.pdf","access_level":"open_access","file_size":4172270}],"date_created":"2021-04-27T08:04:16Z","type":"journal_article","keyword":["network management","service management","coordination","reinforcement learning","self-learning","self-adaptation","multi-objective"],"department":[{"_id":"75"}],"status":"public","has_accepted_license":"1","_id":"21808","publisher":"IEEE","ddc":["000"],"user_id":"35343","file_date_updated":"2021-04-27T08:01:26Z","citation":{"ama":"Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. 2021. doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>","bibtex":"@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021, title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}, DOI={<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>}, journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }","mla":"Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>.","chicago":"Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, 2021. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>.","short":"S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021).","apa":"Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., &#38; Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>","ieee":"S. B. Schneider <i>et al.</i>, “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” <i>Transactions on Network and Service Management</i>, 2021."},"project":[{"name":"SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - Project Area C"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"oa":"1"},{"user_id":"35343","ddc":["004"],"_id":"35889","language":[{"iso":"eng"}],"has_accepted_license":"1","date_updated":"2023-01-10T15:09:05Z","author":[{"full_name":"Schneider, Stefan Balthasar","first_name":"Stefan Balthasar","last_name":"Schneider","orcid":"0000-0001-8210-4011","id":"35343"}],"status":"public","title":"Conventional and Machine Learning Approaches for Network and Service Coordination","year":"2021","department":[{"_id":"75"}],"oa":"1","type":"working_paper","keyword":["nfv","coordination","machine learning","reinforcement learning","phd","digest"],"date_created":"2023-01-10T15:08:50Z","file":[{"date_created":"2023-01-10T15:07:03Z","creator":"stschn","content_type":"application/pdf","file_id":"35890","access_level":"open_access","file_size":133340,"file_name":"main.pdf","date_updated":"2023-01-10T15:07:03Z","relation":"main_file"}],"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"},{"_id":"16","name":"SFB 901 - C4: SFB 901 - Subproject C4"}],"abstract":[{"text":"Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.","lang":"eng"}],"citation":{"ieee":"S. B. Schneider, <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>. 2021.","apa":"Schneider, S. B. (2021). <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>.","chicago":"Schneider, Stefan Balthasar. <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>, 2021.","short":"S.B. Schneider, Conventional and Machine Learning Approaches for Network and Service Coordination, 2021.","mla":"Schneider, Stefan Balthasar. <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>. 2021.","bibtex":"@book{Schneider_2021, title={Conventional and Machine Learning Approaches for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021} }","ama":"Schneider SB. <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>.; 2021."},"file_date_updated":"2023-01-10T15:07:03Z"},{"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:08Z","title":"Every Node for Itself: Fully Distributed Service Coordination","year":"2020","author":[{"first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011","last_name":"Schneider","full_name":"Schneider, Stefan Balthasar","id":"35343"},{"last_name":"Klenner","first_name":"Lars Dietrich","full_name":"Klenner, Lars Dietrich"},{"id":"126","full_name":"Karl, Holger","first_name":"Holger","last_name":"Karl"}],"keyword":["distributed management","service coordination","network coordination","nfv","softwarization","orchestration"],"type":"conference","department":[{"_id":"75"}],"file":[{"date_created":"2020-09-22T06:25:57Z","creator":"stschn","content_type":"application/pdf","file_id":"19608","date_updated":"2020-09-22T06:36:25Z","relation":"main_file","file_size":500948,"access_level":"open_access","file_name":"ris_with_copyright.pdf"}],"date_created":"2020-09-22T06:23:40Z","abstract":[{"text":"Modern services consist of modular, interconnected\r\ncomponents, e.g., microservices forming a service mesh. To\r\ndynamically adjust to ever-changing service demands, service\r\ncomponents have to be instantiated on nodes across the network.\r\nIncoming flows requesting a service then need to be routed\r\nthrough the deployed instances while considering node and link\r\ncapacities. Ultimately, the goal is to maximize the successfully\r\nserved flows and Quality of Service (QoS) through online service\r\ncoordination. Current approaches for service coordination are\r\nusually centralized, assuming up-to-date global knowledge and\r\nmaking global decisions for all nodes in the network. Such global\r\nknowledge and centralized decisions are not realistic in practical\r\nlarge-scale networks.\r\n\r\nTo solve this problem, we propose two algorithms for fully\r\ndistributed service coordination. The proposed algorithms can be\r\nexecuted individually at each node in parallel and require only\r\nvery limited global knowledge. We compare and evaluate both\r\nalgorithms with a state-of-the-art centralized approach in extensive\r\nsimulations on a large-scale, real-world network topology.\r\nOur results indicate that the two algorithms can compete with\r\ncentralized approaches in terms of solution quality but require\r\nless global knowledge and are magnitudes faster (more than\r\n100x).","lang":"eng"}],"publication":"IEEE International Conference on Network and Service Management (CNSM)","user_id":"35343","ddc":["006"],"_id":"19607","publisher":"IEEE","has_accepted_license":"1","status":"public","oa":"1","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"file_date_updated":"2020-09-22T06:36:25Z","citation":{"ieee":"S. B. Schneider, L. D. Klenner, and H. Karl, “Every Node for Itself: Fully Distributed Service Coordination,” in <i>IEEE International Conference on Network and Service Management (CNSM)</i>, 2020.","apa":"Schneider, S. B., Klenner, L. D., &#38; Karl, H. (2020). Every Node for Itself: Fully Distributed Service Coordination. In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE.","short":"S.B. Schneider, L.D. Klenner, H. Karl, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.","chicago":"Schneider, Stefan Balthasar, Lars Dietrich Klenner, and Holger Karl. “Every Node for Itself: Fully Distributed Service Coordination.” In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE, 2020.","mla":"Schneider, Stefan Balthasar, et al. “Every Node for Itself: Fully Distributed Service Coordination.” <i>IEEE International Conference on Network and Service Management (CNSM)</i>, IEEE, 2020.","bibtex":"@inproceedings{Schneider_Klenner_Karl_2020, title={Every Node for Itself: Fully Distributed Service Coordination}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}, year={2020} }","ama":"Schneider SB, Klenner LD, Karl H. Every Node for Itself: Fully Distributed Service Coordination. In: <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE; 2020."}},{"user_id":"35343","ddc":["006"],"_id":"19609","publisher":"IEEE","has_accepted_license":"1","status":"public","oa":"1","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"file_date_updated":"2020-09-22T06:36:00Z","citation":{"mla":"Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” <i>IEEE International Conference on Network and Service Management (CNSM)</i>, IEEE, 2020.","ama":"Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE; 2020.","bibtex":"@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020, title={Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2020} }","apa":"Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili, R., &#38; Hecker, A. (2020). Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE.","ieee":"S. B. Schneider <i>et al.</i>, “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Network and Service Management (CNSM)</i>, 2020.","chicago":"Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE, 2020.","short":"S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili, A. Hecker, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020."},"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:08Z","title":"Self-Driving Network and Service Coordination Using Deep Reinforcement Learning","year":"2020","author":[{"full_name":"Schneider, Stefan Balthasar","last_name":"Schneider","first_name":"Stefan Balthasar","orcid":"0000-0001-8210-4011","id":"35343"},{"full_name":"Manzoor, Adnan","first_name":"Adnan","last_name":"Manzoor"},{"full_name":"Qarawlus, Haydar","first_name":"Haydar","last_name":"Qarawlus"},{"full_name":"Schellenberg, Rafael","first_name":"Rafael","last_name":"Schellenberg"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"},{"first_name":"Ramin","last_name":"Khalili","full_name":"Khalili, Ramin"},{"last_name":"Hecker","first_name":"Artur","full_name":"Hecker, Artur"}],"type":"conference","keyword":["self-driving networks","self-learning","network coordination","service coordination","reinforcement learning","deep learning","nfv"],"department":[{"_id":"75"}],"file":[{"date_created":"2020-09-22T06:29:16Z","creator":"stschn","content_type":"application/pdf","file_id":"19610","access_level":"open_access","file_size":642999,"file_name":"ris_with_copyright.pdf","date_updated":"2020-09-22T06:36:00Z","relation":"main_file"}],"date_created":"2020-09-22T06:28:22Z","abstract":[{"text":"Modern services comprise interconnected components,\r\ne.g., microservices in a service mesh, that can scale and\r\nrun on multiple nodes across the network on demand. To process\r\nincoming traffic, service components have to be instantiated and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands into account. This challenge is usually solved with\r\ncustom approaches designed by experts. While this typically\r\nworks well for the considered scenario, the models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement learning approach that\r\nlearns how to best coordinate services and is geared towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable, possibly delayed monitoring information. Rather than\r\ndefining a complex model or an algorithm how to achieve an\r\nobjective, our model-free approach adapts to various objectives\r\nand traffic patterns. An agent is trained offline without expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and overall network utility on real-world network\r\ntopologies and traffic traces. It also learns to optimize different\r\nobjectives, generalizes to scenarios with unseen, stochastic traffic\r\npatterns, and scales to large real-world networks.","lang":"eng"}],"publication":"IEEE International Conference on Network and Service Management (CNSM)"},{"extern":"1","abstract":[{"text":"When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system which we develop in joint cooperation with a disaster management authority and a software development company.","lang":"eng"}],"publication":"Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management","department":[{"_id":"277"}],"keyword":["Coordination of spontaneous volunteers","volunteer coordination system","decision support","scheduling optimization model","linear programming"],"type":"conference","date_created":"2018-11-14T15:35:54Z","file":[{"content_type":"application/pdf","file_id":"6020","access_level":"open_access","file_size":488472,"file_name":"2018_ISCRAM_Conference_Proceedings - Publication Version.pdf","date_updated":"2018-12-13T15:05:44Z","relation":"main_file","date_created":"2018-12-07T11:25:06Z","creator":"hsiemes"}],"date_updated":"2022-01-06T07:02:28Z","author":[{"full_name":"Rauchecker, Gerhard","first_name":"Gerhard","last_name":"Rauchecker"},{"id":"72850","full_name":"Schryen, Guido","first_name":"Guido","last_name":"Schryen"}],"year":"2018","title":"Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief","language":[{"iso":"eng"}],"citation":{"ama":"Rauchecker G, Schryen G. Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief. In: <i>Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management</i>. ; 2018.","bibtex":"@inproceedings{Rauchecker_Schryen_2018, title={Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief}, booktitle={Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management}, author={Rauchecker, Gerhard and Schryen, Guido}, year={2018} }","mla":"Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief.” <i>Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management</i>, 2018.","chicago":"Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief.” In <i>Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management</i>, 2018.","short":"G. Rauchecker, G. Schryen, in: Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management, 2018.","apa":"Rauchecker, G., &#38; Schryen, G. (2018). Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief. In <i>Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management</i>. Rochester, NY, USA.","ieee":"G. Rauchecker and G. Schryen, “Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief,” in <i>Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management</i>, Rochester, NY, USA, 2018."},"file_date_updated":"2018-12-13T15:05:44Z","oa":"1","has_accepted_license":"1","conference":{"name":"15th International Conference on Information Systems for Crisis Response and Management","location":"Rochester, NY, USA"},"status":"public","user_id":"61579","ddc":["000"],"_id":"5675"},{"citation":{"apa":"Pitsch, K., Vollmer, A.-L., Rohlfing, K., Fritsch, J., &#38; Wrede, B. (2014). Tutoring in adult-child-interaction: On the loop of the tutor’s action modification and the recipient’s gaze. <i>Interaction Studies</i>, <i>15</i>(1), 55–98. <a href=\"https://doi.org/10.1075/is.15.1.03pit\">https://doi.org/10.1075/is.15.1.03pit</a>","mla":"Pitsch, Karola, et al. “Tutoring in Adult-Child-Interaction: On the Loop of the Tutor’s Action Modification and the Recipient’s Gaze.” <i>Interaction Studies</i>, vol. 15, no. 1, John Benjamins Publishing Company, 2014, pp. 55–98, doi:<a href=\"https://doi.org/10.1075/is.15.1.03pit\">10.1075/is.15.1.03pit</a>.","ieee":"K. Pitsch, A.-L. Vollmer, K. Rohlfing, J. Fritsch, and B. Wrede, “Tutoring in adult-child-interaction: On the loop of the tutor’s action modification and the recipient’s gaze,” <i>Interaction Studies</i>, vol. 15, no. 1, pp. 55–98, 2014, doi: <a href=\"https://doi.org/10.1075/is.15.1.03pit\">10.1075/is.15.1.03pit</a>.","short":"K. Pitsch, A.-L. Vollmer, K. Rohlfing, J. Fritsch, B. Wrede, Interaction Studies 15 (2014) 55–98.","ama":"Pitsch K, Vollmer A-L, Rohlfing K, Fritsch J, Wrede B. Tutoring in adult-child-interaction: On the loop of the tutor’s action modification and the recipient’s gaze. <i>Interaction Studies</i>. 2014;15(1):55-98. doi:<a href=\"https://doi.org/10.1075/is.15.1.03pit\">10.1075/is.15.1.03pit</a>","chicago":"Pitsch, Karola, Anna-Lisa Vollmer, Katharina Rohlfing, Jannik Fritsch, and Britta Wrede. “Tutoring in Adult-Child-Interaction: On the Loop of the Tutor’s Action Modification and the Recipient’s Gaze.” <i>Interaction Studies</i> 15, no. 1 (2014): 55–98. <a href=\"https://doi.org/10.1075/is.15.1.03pit\">https://doi.org/10.1075/is.15.1.03pit</a>.","bibtex":"@article{Pitsch_Vollmer_Rohlfing_Fritsch_Wrede_2014, title={Tutoring in adult-child-interaction: On the loop of the tutor’s action modification and the recipient’s gaze}, volume={15}, DOI={<a href=\"https://doi.org/10.1075/is.15.1.03pit\">10.1075/is.15.1.03pit</a>}, number={1}, journal={Interaction Studies}, publisher={John Benjamins Publishing Company}, author={Pitsch, Karola and Vollmer, Anna-Lisa and Rohlfing, Katharina and Fritsch, Jannik and Wrede, Britta}, year={2014}, pages={55–98} }"},"_id":"17199","publisher":"John Benjamins Publishing Company","page":"55-98","volume":15,"user_id":"14931","status":"public","date_created":"2020-06-24T13:01:17Z","department":[{"_id":"749"}],"type":"journal_article","keyword":["conversation analysis","interactional coordination","adult-child-interaction","feedback","gaze","quantification","social learning","motionese","tutoring"],"publication":"Interaction Studies","issue":"1","abstract":[{"lang":"eng","text":"Research of tutoring in parent-infant interaction has shown that tutors - when presenting some action - modify both their verbal and manual performance for the learner (‘motherese’, ‘motionese’). Investigating the sources and effects of the tutors’ action modifications, we suggest an interactional account of ‘motionese’. Using video-data from a semi-experimental study in which parents taught their 8 to 11 month old infants how to nest a set of differently sized cups, we found that the tutors’ action modifications (in particular: high arches) functioned as an orienting device to guide the infant’s visual attention (gaze). Action modification and the recipient’s gaze can be seen to have a reciprocal sequential relationship and to constitute a constant loop of mutual adjustments. Implications are discussed for developmental research and for robotic ‘Social Learning’. We argue that a robot system could use on-line feedback strategies (e.g. gaze) to pro-actively shape a tutor’s action presentation as it emerges."}],"language":[{"iso":"eng"}],"doi":"10.1075/is.15.1.03pit","publication_identifier":{"issn":["1572-0381"]},"author":[{"first_name":"Karola","last_name":"Pitsch","full_name":"Pitsch, Karola"},{"first_name":"Anna-Lisa","last_name":"Vollmer","full_name":"Vollmer, Anna-Lisa"},{"id":"50352","first_name":"Katharina","last_name":"Rohlfing","full_name":"Rohlfing, Katharina"},{"full_name":"Fritsch, Jannik","first_name":"Jannik","last_name":"Fritsch"},{"full_name":"Wrede, Britta","last_name":"Wrede","first_name":"Britta"}],"year":"2014","title":"Tutoring in adult-child-interaction: On the loop of the tutor's action modification and the recipient's gaze","intvolume":"        15","date_updated":"2023-02-01T16:10:52Z"}]