In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce the uncertainty affecting SAS (other than through the blanket monitoring of their components and environment) remain underexplored. Our paper proposes a more nuanced, adaptive approach to SAS uncertainty reduction. To that end, we introduce a SAS architecture comprising an uncertainty reduction controller that drives the adaptive acquisition of new information within the SAS adaptation loop, and a tool-supported method that uses probabilistic model checking to synthesise such controllers. The controllers generated by our method deliver optimal trade-offs between SAS uncertainty reduction benefits and new information acquisition costs. We illustrate the use and evaluate the effectiveness of our approach for mobile robot navigation and server infrastructure management SAS.

翻译：在自适应性系统（SAS）应对不确定性的方法探索中，研究社区主要聚焦于通过调整SAS架构或行为来应对不确定性的解决方案。相比之下，旨在降低影响SAS的不确定性（而非通过对其组件和环境的全面监控）的解决方案仍待深入探索。本文提出了一种更具细粒度、自适应性的SAS不确定性抑制方法。为此，我们引入了一种包含不确定性抑制控制器的SAS架构，该控制器能够驱动SAS自适应循环中新型信息的自适应获取，同时提供了一种基于概率模型检测的工具化方法来实现此类控制器的综合。通过该方法生成的控制器能够在SAS不确定性抑制效益与信息获取成本之间实现最优权衡。我们通过移动机器人导航与服务器基础设施管理两类SAS案例，演示了该方法的应用并评估了其有效性。