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Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems
DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals.
On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems
Yet, since machine learning methods apply in essence statistical methods, they may have an impact on the decisions made by a self-adaptive system.
Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review
The research questions are centred on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges.
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