控制理论与AI相结合的工程自适应系统混合方法
A Hybrid Approach Combining Control Theory and AI for Engineering Self-Adaptive Systems
摘要：
控制理论技术已经成功地被采用作为自适应系统设计的方法，以提供自适应机制的有效性和鲁棒性的形式保证。然而，当涉及到动态适应时，获得保证的计算工作带来了严重的限制。为了解决这些局限性，在本文中，我们提出了一种混合的方法结合软件工程，控制理论，和人工智能设计的软件自适应。我们的解决方案提出了一个层次化和动态的系统管理器与性能调优。由于高层需求规范和被管理系统的内部旋钮行为之间的差距，分层组成的组件体系结构寻求关注点的分离，以实现动态解决方案。因此，设计了一个两层的自适应管理器，通过回归分析和进化元启发式参数优化，以满足软件需求。优化依赖于性能、有效性和鲁棒性指标的收集和处理，以控制离线和在线阶段的理论指标。我们评估我们的工作与原型的身体传感器网络（BSN）在医疗保健领域，这在很大程度上是作为一个示范由社区。BSN是在机器人操作系统（ROS）架构下实现的，并考虑系统的可靠性作为自适应目标。我们的研究结果加强了在这样一个安全关键领域表现良好的必要性，并提供了大量证据，说明将控制和基于人工智能的技术相结合的混合方法如何用于工程自适应系统，可以提供有效的适应。
英文摘要：
Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r.t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
