34 citations · 70 across the 7 of their papers we have counts for
8 papers
Towards the decentralized coordination of multiple self-adaptive systems
Paul-Andrei Dragan, Andreas Metzger, Klaus Pohl
When multiple self-adaptive systems share the same environment and have common goals, they may coordinate their adaptations at runtime to avoid conflicts and to satisfy their goals…
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…
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…
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…
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
Pooyan Jamshidi, Amir Sharifloo, Claus Pahl +2
Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Exi…