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系统架构或行为来响应不确定性的方案。相比之下，减少影响SAS的不确定性（除通过对其组件和环境进行全面监控外）的解决方案仍未得到充分探索。本文提出一种更精细的自适应方法来减少SAS中的不确定性。为此，我们引入一种包含不确定性缩减控制器的SAS架构，该控制器能驱动SAS自适应循环中新信息的自适应获取，并提供一种基于工具的方法，通过概率模型检测来综合此类控制器。我们的方法生成的控制器能在SAS不确定性缩减效益与新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和服务器基础设施管理SAS实例展示了该方法的使用并评估了其有效性。