34 citations · 71 across the 6 of their papers we have counts for
6 papers
Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring
Andreas Metzger, Tristan Kley, Aristide Rothweiler +1
Prescriptive business process monitoring provides decision support to process managers on when and how to adapt an ongoing business process to prevent or mitigate an undesired proc…
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…
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…
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems
Felix Feit, Andreas Metzger, Klaus Pohl
Design time uncertainty poses an important challenge when developing a self-adaptive system. As an example, defining how the system should adapt when facing a new environment state…
Counterfactual Explanations for Predictive Business Process Monitoring
Tsung-Hao Huang, Andreas Metzger, Klaus Pohl
Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than si…
Feature-Model-Guided Online Learning for Self-Adaptive Systems
Andreas Metzger, Clément Quinton, Zoltán Ádám Mann +2
A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptiv…