Search Results for author: Omid Gheibi

Found 5 papers, 0 papers with code

Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation

no code implementations4 Nov 2022 Omid Gheibi, Danny Weyns

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.

Decision Making Self Adaptive System

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems

no code implementations13 Apr 2022 Danny Weyns, Omid Gheibi, Federico Quin, Jeroen Van Der Donckt

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.

Feature Engineering Self Adaptive System

Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning

no code implementations4 Apr 2022 Omid Gheibi, Danny Weyns

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.

BIG-bench Machine Learning Decision Making +1

On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems

no code implementations18 Mar 2021 Omid Gheibi, Danny Weyns, Federico Quin

Yet, since machine learning methods apply in essence statistical methods, they may have an impact on the decisions made by a self-adaptive system.

BIG-bench Machine Learning Decision Making +2

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

no code implementations6 Mar 2021 Omid Gheibi, Danny Weyns, Federico Quin

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.

BIG-bench Machine Learning

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