论文标题
终身自我适应：自我适应符合终身的机器学习
Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning
论文作者
论文摘要
在过去的几年中，机器学习（ML）已成为支持自我适应的一种流行方法。尽管ML技术可以处理自我适应的几个问题，例如可扩展的决策，但它们也应面临固有的挑战。在本文中，我们专注于一个对于自我适应尤其重要的挑战：ML技术旨在处理与操作领域相关的一组预定义的任务；他们有解决新的新任务的问题，例如用于学习的输入数据中的概念转移。为了应对这一挑战，我们提出\ textit {终生自我适应}：一种新型的自我适应方法，可以增强使用ML技术终身ML层的自适应系统。终生的ML层跟踪运行系统及其环境，将这些知识与当前任务相关联，基于区分确定新任务，并相应地更新自适应系统的学习模型。我们提出了可重复使用的体系结构，用于终身自我适应，并将其应用于由学习模型的输入数据的变化引起的概念漂移的情况，该模型用于自我适应的决策。我们使用两种情况来验证两种类型的概念漂移的终身自我适应。
In the past years, machine learning (ML) has become a popular approach to support self-adaptation. While ML techniques enable dealing with several problems in self-adaptation, such as scalable decision-making, they are also subject to inherent challenges. 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. To tackle this challenge, we present \textit{lifelong self-adaptation}: a novel approach to self-adaptation that enhances self-adaptive systems that use ML techniques with a lifelong ML layer. The lifelong ML layer tracks the running system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differentiations, and updates the learning models of the self-adaptive system accordingly. We present a reusable architecture for lifelong self-adaptation and apply it to the case of concept drift caused by unforeseen changes of the input data of a learning model that is used for decision-making in self-adaptation. We validate lifelong self-adaptation for two types of concept drift using two cases.
