论文标题
部分可观测时空混沌系统的无模型预测
Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation
论文作者
论文摘要
最近，机器学习（ML）已成为支持自我适应的流行方法。 ML已被用来处理自我适应的几个问题，例如在不确定性和可扩展决策下保持最新的运行时模型。但是，利用ML带来了固有的挑战。在本文中，我们关注基于学习的自适应系统的特别重要挑战：适应空间中的漂移。使用适应空间，我们参考了自适应系统可以在给定时间选择的一组适应选项，以根据适应选项的估计质量属性进行适应。适应空间的漂移源自不确定性，影响适应选项的质量特性。这种漂移可能意味着最终没有适应选项可以满足适应目标的初始集合，使系统质量恶化，或者可能会出现适应选项，从而可以增强适应性目标。在ML中，这种转变对应于新型的班级外观，这是目标数据中的一种概念漂移，常见ML技术会遇到问题。为了解决这个问题，我们提出了一种新型的自我适应方法，该方法可以通过终身ML层增强基于学习的自适应系统。我们将这种方法称为终身自我适应。终身ML层跟踪系统及其环境，将这些知识与当前任务相关联，根据差异确定新任务，并相应地更新自适应系统的学习模型。人类利益相关者可能会参与支持学习过程并调整学习和目标模型。我们提出了终身自我适应的一般体系结构，并将其应用于影响自我适应决策的适应空间漂移的情况。我们使用Deltaiot示例验证了一系列场景的方法。
Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this paper, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space we refer to the set of adaptation options a self-adaptive system can select from at a given time to adapt based on the estimated quality properties of the adaptation options. Drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that eventually no adaptation option can satisfy the initial set of the adaptation goals, deteriorating the quality of the system, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such shift corresponds to novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. 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. We validate the approach for a series of scenarios using the DeltaIoT exemplar.
