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2020 – today
- 2024
[c7]Eric Wete, Joel Greenyer, Daniel Kudenko, Wolfgang Nejdl:
Multi-Robot Motion and Task Planning in Automotive Production Using Controller-based Safe Reinforcement Learning. AAMAS 2024: 1928-1937- 2023
- [c6]Eric Wete
, Joel Greenyer, Andreas Wortmann, Daniel Kudenko, Wolfgang Nejdl:
MDE and Learning for flexible Planning and optimized Execution of Multi-Robot Choreographies. ETFA 2023: 1-4 - 2022
- [c5]Eric Wete, Joel Greenyer, Daniel Kudenko, Wolfgang Nejdl, Oliver Flegel, Dennes Eisner:
A tool for the automation of efficient multi-robot choreography planning and execution. MoDELS (Companion) 2022: 37-41 - 2021
- [c4]Eric Wete, Joel Greenyer, Andreas Wortmann, Oliver Flegel, Martin Klein:
Monte Carlo Tree Search and GR(1) Synthesis for Robot Tasks Planning in Automotive Production Lines. MoDELS 2021: 320-330
2010 – 2019
- 2018
- [c3]Joel Greenyer, Larissa Chazette, Daniel Gritzner, Eric Wete:
A Scenario-Based MDE Process for Dynamic Topology Collaborative Reactive Systems - Early Virtual Prototyping of Car-to-X System Specifications. Modellierung (Workshops) 2018: 111-120 - 2017
- [c2]Joel Greenyer, Daniel Gritzner, Jianwei Shi, Eric Wete:
A Scenario-based MDE Process for Developing Reactive Systems: A Cleaning Robot Example. MoDELS (Satellite Events) 2017: 355-359 - [c1]Joel Greenyer, Daniel Gritzner, Florian König, Jannik Dahlke, Jianwei Shi, Eric Wete:
From scenario modeling to scenario programming for reactive systems with dynamic topology. ESEC/SIGSOFT FSE 2017: 974-978
Coauthor Index
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