This year, the 3rd special edition of the Models@run.time Workshop for Self-aware Computing Systems and the 2nd edition of the Workshop for Self-aware Computing will be jointly organized.
While the focus of the Models at run.time workshop series is to provide a forum for exchange of ideas on the use of run-time models, the workshop on self-aware computing systems covers the interdisciplinary area of self-aware computing, fostering interaction and collaborations between the different research communities interested in self-aware computing systems.
As documented in a recent Springer book on the topic, self-aware computing systems are understood in a broad sense seeking to integrate the different ways in which this term is used in the interdisciplinary research landscape.
More specifically, self-aware computing systems are understood as having two main properties. They:
- learn models, capturing knowledge about themselves and their environment (such as their structure, design, state, possible actions, and runtime behavior) on an ongoing basis; and
- reason using the models (to predict, analyze, consider, or plan), which enables them to act based on their knowledge and reasoning (for example, to explore, explain, report, suggest, self-adapt, or impact their environment),
and do so in accordance with high-level goals, which can change.
- Sebastian Götz, Technische Universität Dresden, Germany
- Nikolas Herbst , University of Würzburg, Germany
- Nelly Bencomo, Aston Universiy, UK
- Kirstie L. Bellman, Topcy House Consulting, US
- Peter Lewis, Aston University, UK
- Javier Camara Moreno, Carnegie Mellon University, Pittsburgh, US