Models@ run. time: a guided tour of the state of the art and research challenges
More than a decade ago, the research topic models@ run. time was coined. Since then, the
research area has received increasing attention. Given the prolific results during these …
research area has received increasing attention. Given the prolific results during these …
The application of machine learning in self-adaptive systems: A systematic literature review
TRD Saputri, SW Lee - IEEE Access, 2020 - ieeexplore.ieee.org
Context: Self-adaptive systems have been studied in software engineering over the past few
decades attempting to address challenges within the field. There is a continuous significant …
decades attempting to address challenges within the field. There is a continuous significant …
Proactive self-adaptation under uncertainty: a probabilistic model checking approach
Self-adaptive systems tend to be reactive and myopic, adapting in response to changes
without anticipating what the subsequent adaptation needs will be. Adapting reactively can …
without anticipating what the subsequent adaptation needs will be. Adapting reactively can …
Transfer learning for improving model predictions in highly configurable software
Modern software systems are built to be used in dynamic environments using configuration
capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we …
capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we …
Machine learning meets quantitative planning: Enabling self-adaptation in autonomous robots
Modern cyber-physical systems (eg, robotics systems) are typically composed of physical
and software components, the characteristics of which are likely to change over time …
and software components, the characteristics of which are likely to change over time …
Quantitative verification-aided machine learning: A tandem approach for architecting self-adaptive IoT systems
J Cámara, H Muccini… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Architecting IoT systems able to guarantee Quality of Service (QoS) levels can be a
challenging task due to the inherent uncertainties (induced by changes in eg, energy …
challenging task due to the inherent uncertainties (induced by changes in eg, energy …
Deep learning for effective and efficient reduction of large adaptation spaces in self-adaptive systems
Many software systems today face uncertain operating conditions, such as sudden changes
in the availability of resources or unexpected user behavior. Without proper mitigation these …
in the availability of resources or unexpected user behavior. Without proper mitigation these …
[图书][B] Autonomous horizons: the way forward
GL Zacharias - 2019 - apps.dtic.mil
Dr. Greg Zacharias, Chief Scientist of the United States Air Force 2015-18, explores next
steps in autonomous systems AS development, fielding, and training. Rapid advances in AS …
steps in autonomous systems AS development, fielding, and training. Rapid advances in AS …
Supporting remote real-time expert help: Opportunities and challenges for novice 3d modelers
We investigate how novice 3D modelers can remotely leverage real-time expert help to aid
their learning tasks. We first carried out an observational study of remote novice-expert pairs …
their learning tasks. We first carried out an observational study of remote novice-expert pairs …
Learning self-adaptations for iot networks: A genetic programming approach
J Li, S Nejati, M Sabetzadeh - Proceedings of the 17th Symposium on …, 2022 - dl.acm.org
Internet of Things (IoT) is a pivotal technology in application domains that require
connectivity and interoperability between large numbers of devices. IoT systems …
connectivity and interoperability between large numbers of devices. IoT systems …