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MDE and Learning for flexible Planning and optimized Execution of Multi-Robot Choreographies

E. Wete, J. Greenyer, A. Wortmann, D. Kudenko, and W. Nejdl. 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), page 1--4. IEEE, (2023)

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Univ. -Prof. Dr. rer. nat. Eric Lutz University of Stuttgart

Extracting Bayesian networks from multiple copies of a quantum systemK. Micadei, G. Landi, and E. Lutz. epl, 144 (6): 60002 (2024)

Eric Steinbrücker University of Stuttgart

Dr. -Ing. Eric Jennes University of Stuttgart

Evariste Pasky Wete University of Stuttgart

Eric Nkrumah University of Stuttgart

Other publications of authors with the same name

A Scenario-based MDE Process for Developing Reactive Systems: A Cleaning Robot Example.J. Greenyer, D. Gritzner, J. Shi, and E. Wete. MoDELS (Satellite Events), volume 2019 of CEUR Workshop Proceedings, page 355-359. CEUR-WS.org, (2017)Monte Carlo Tree Search and GR(1) Synthesis for Robot Tasks Planning in Automotive Production LinesE. Wete, J. Greenyer, A. Wortmann, O. Flegel, and M. Klein. 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), page 320-330. Piscataway, IEEE, (2021)A Scenario-Based MDE Process for Dynamic Topology Collaborative Reactive Systems - Early Virtual Prototyping of Car-to-X System Specifications.J. Greenyer, L. Chazette, D. Gritzner, and E. Wete. Modellierung (Workshops), volume 2060 of CEUR Workshop Proceedings, page 111-120. CEUR-WS.org, (2018)From scenario modeling to scenario programming for reactive systems with dynamic topology.J. Greenyer, D. Gritzner, F. König, J. Dahlke, J. Shi, and E. Wete. ESEC/SIGSOFT FSE, page 974-978. ACM, (2017)MDE and Learning for flexible Planning and optimized Execution of Multi-Robot ChoreographiesE. Wete, J. Greenyer, A. Wortmann, D. Kudenko, and W. Nejdl. 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), page 1--4. IEEE, (2023)