This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …

We launch the new probabilistic model checker S torm. It features the analysis of discrete-
and continuous-time variants of both Markov chains and MDPs. It supports the P rism and …

The process of scaling microservices is a challenging task, especially in maintaining
optimum resource provisioning while respecting QoS constraints and SLA. Many research …

We present automatic verification techniques for the modelling and analysis of probabilistic
systems that incorporate competitive behaviour. These systems are modelled as turn-based …

This paper describes results from evaluating different self-localization approaches in indoor
environments for mobile robots. The algorithms examined are based on 2D laser scans and …

We propose an approach to synthesize control protocols for autonomous systems that
account for uncertainties and imperfections in interactions with human operators. As an …

We propose a conceptually simple technique for verifying probabilistic models whose
transition probabilities are parametric. The key is to replace parametric transitions by …

Assistive robotic systems are quickly becoming a core technology for the service sector as
they are understood capable of supporting people in need of assistance in a wide variety of …

The software engineering community has proposed numerous approaches for making
software self-adaptive. These approaches take inspiration from machine learning and …

Self-adaptive systems overcome many of the limitations of human supervision in complex
software-intensive systems by endowing them with the ability to automatically adapt their …