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Online ML Self-adaptation in Face of Traps
Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties -- traps -- that, to our knowledge, are not discussed enough in the community.
From Self-Adaptation to Self-Evolution Leveraging the Operational Design Domain
Then, we outline a new approach for self-evolution that leverages the concept of ODD, enabling a system to evolve autonomously to deal with conditions not anticipated by its initial ODD.
Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation
We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation.
The Vision of Self-Evolving Computing Systems
We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions.
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.
Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning
In this paper, we focus on one such challenge that is particularly important for self-adaptation: ML techniques are designed to deal with a set of predefined tasks associated with an operational domain; they have problems to deal with new emerging tasks, such as concept shift in input data that is used for learning.
Lifelong Computing
When detecting anomalies, novelties, new goals or constraints, a lifelong computing system activates an evolutionary self-learning engine that runs online experiments to determine how the computing-learning system needs to evolve to deal with the changes, thereby changing its architecture and integrating new computing elements from computing warehouses as needed.
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.
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.
