TY - JOUR A1 - Anders, Gerrit A1 - Schiendorfer, Alexander A1 - Siefert, Florian A1 - Steghöfer, Jan-Philipp A1 - Reif, Wolfgang T1 - Cooperative Resource Allocation in Open Systems of Systems JF - ACM Transactions on Autonomous and Adaptive Systems UR - https://dl.acm.org/doi/10.1145/2700323 Y1 - 2015 UR - https://dl.acm.org/doi/10.1145/2700323 SN - 1556-4665 SN - 1556-4703 VL - 10 IS - 2 PB - ACM CY - New York ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Steghöfer, Jan-Philipp A1 - Knapp, Alexander A1 - Nafz, Florian A1 - Reif, Wolfgang ED - Bramer, Max ED - Petridis, Miltos T1 - Constraint Relationships for Soft Constraints T2 - Research and Development in Intelligent Systems XXX: Incorporating Applications and Innovations in Intelligent Systems XXI: Proceedings of AI-2013 UR - https://doi.org/10.1007/978-3-319-02621-3_17 Y1 - 2013 UR - https://doi.org/10.1007/978-3-319-02621-3_17 SN - 978-3-319-02621-3 SP - 241 EP - 255 PB - Springer CY - Cham ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Steghöfer, Jan-Philipp A1 - Reif, Wolfgang T1 - Synthesis and Abstraction of Constraint Models for Hierarchical Resource Allocation Problems T2 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence: Volume 2: ICAART N2 - Many resource allocation problems are hard to solve even with state-of-the-art constraint optimisation software upon reaching a certain scale. Our approach to deal with this increasing complexity is to employ a hierarchical “regio-central” mechanism. It requires two techniques: (1) the synthesis of several models of agents providing a certain resource into a centrally and efficiently solvable optimisation problem and (2) the creation of an abstracted version of this centralised model that reduces its complexity when passing it on to higher layers. We present algorithms to create such synthesised and abstracted models in a fully automated way and demonstrate empirically that the obtained solutions are comparable to central solutions but scale better in an example taken from energy management. UR - https://doi.org/10.5220/0004757700150027 Y1 - 2014 UR - https://doi.org/10.5220/0004757700150027 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42364 SN - 978-989-758-016-1 SP - 15 EP - 27 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Knapp, Alexander A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang T1 - Quality over Quantity in Soft Constraints T2 - Proceedings: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence: ICTAI 2014 UR - https://doi.org/10.1109/ICTAI.2014.75 Y1 - 2014 UR - https://doi.org/10.1109/ICTAI.2014.75 SN - 978-1-4799-6572-4 SP - 453 EP - 460 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Anders, Gerrit A1 - Schiendorfer, Alexander A1 - Steghöfer, Jan-Philipp A1 - Reif, Wolfgang ED - Stechele, Walter ED - Wild, Thomas T1 - Robust Scheduling in a Self-Organizing Hierarchy of Autonomous Virtual Power Plants T2 - ARCS 2014 - 27th International Conference on Architecture of Computing Systems: Workshop Proceedings Y1 - 2014 UR - https://www.vde-verlag.de/proceedings-en/563579019.html SN - 978-3-8007-3579-2 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/document/6775090 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Lassner, Christoph A1 - Anders, Gerrit A1 - Reif, Wolfgang A1 - Lienhart, Rainer T1 - Active Learning for Efficient Sampling of Control Models of Collectives T2 - Proceedings: 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems: SASO 2015 UR - https://doi.org/10.1109/SASO.2015.13 Y1 - 2015 UR - https://doi.org/10.1109/SASO.2015.13 SP - 51 EP - 60 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Wautelet, Yves A1 - Reif, Wolfgang ED - Loiseau, Stephane ED - Filipe, Joaquim ED - Duval, Béatrice ED - van den Herik, Jaap T1 - Modeling Hierarchical Resources Within a Unified Ontology BT - A Position Paper T2 - Proceedings of the International Conference on Agents and Artificial Intelligence N2 - Resource-Intensive Software Ecosystems (RISE) can mainly be found in production management but also in virtually any socio-technical environment. RISE appear prominently in the form of smart grids or cloud environments where optimizing resource utilization and allocation becomes the most important aspect for competitive service provision. In such a context, the need for unified ontologies supported by adaptive software (i.e., software able to learn from and act on its environment) is highly attractive. Indeed, resources are mostly not monolithic entities but active and collaborative agents often organized in a hierarchical manner. A hierarchy implies multiple levels of abstraction leading to resource allocation on different levels of organization -- with abstractions being relevant for both inter- and intra-organization resource management. Once adequately defined, the use of constraint-based optimization algorithms on those multiple levels can provide efficient resource allocation. We apply, in this paper, ontological elements to model resources in a unified manner on multiple levels onto an example taken from distributed energy management. Then we present algorithmic ideas to organize the hierarchy of these resources. UR - https://doi.org/10.5220/0005289006140619 Y1 - 2015 UR - https://doi.org/10.5220/0005289006140619 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42448 SN - 978-989-758-074-1 SP - 614 EP - 619 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Steghöfer, Jan-Philipp A1 - Reif, Wolfgang T1 - Synthesised Constraint Models for Distributed Energy Management T2 - Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS) Y1 - 2014 UR - https://ieeexplore.ieee.org/document/6933199 SN - 978-83-60810-58-3 SP - 1529 EP - 1538 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schiendorfer, Alexander A1 - Knapp, Alexander A1 - Anders, Gerrit A1 - Reif, Wolfgang T1 - MiniBrass: Soft constraints for MiniZinc JF - Constraints UR - https://doi.org/10.1007/s10601-018-9289-2 KW - Soft constraints KW - Modeling languages KW - MiniZinc Y1 - 2018 UR - https://doi.org/10.1007/s10601-018-9289-2 SN - 1572-9354 VL - 23 IS - 4 SP - 403 EP - 450 PB - Springer CY - Dodrecht ER - TY - CHAP A1 - Krützmann, Julia A1 - Schiendorfer, Alexander A1 - Beratz, Sergej A1 - Moosburger-Will, Judith A1 - Reif, Wolfgang A1 - Horn, Siegfried ED - Nicosia, Giuseppe ED - Ojha, Varun ED - La Malfa, Emanuele ED - Jansen, Giorgio ED - Sciacca, Vincenzo ED - Pardalos, Panos ED - Giuffrida, Giovanni ED - Umeton, Renato T1 - Learning Controllers for Adaptive Spreading of Carbon Fiber Tows T2 - Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020, Siena, Italy, July 19-23, 2020, Revised Selected Papers, Part II UR - https://doi.org/10.1007/978-3-030-64580-9_6 Y1 - 2020 UR - https://doi.org/10.1007/978-3-030-64580-9_6 SN - 978-3-030-64580-9 SN - 978-3-030-64579-3 SP - 65 EP - 77 PB - Springer CY - Cham ER -