1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Andreas Metzger, Jone Bartel, Jan Laufer
Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as…
cs.SE2023
A User Study on Explainable Online Reinforcement Learning for Adaptive Systems
Andreas Metzger, Jan Laufer, Felix Feit +1
Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty. Online RL facilitates learning from actual operat…
cs.SE2022★ 1 cited
UMLsec4Edge: Extending UMLsec to model data-protection-compliant edge computing systems
Sven Smolka, Jan Laufer, Zoltán Ádám Mann +1
Edge computing enables the processing of data - frequently personal data - at the edge of the network. For personal data, legislation such as the European General Data Protection R…