Reasoning about When to Provide Explanation for Human-in-the-loop Self-Adaptive Systems

Many self-adaptive systems benefit from human
involvement, where a human operator can provide expertise not available to the system and perform adaptations involving physical changes that cannot be automated. However, a lack
of transparency and intelligibility of system goals and the autonomous behaviors enacted to achieve them may hinder a human operator's effort to make such involvement effective. Explanation
is sometimes helpful to allow the human to understand why the system is making certain decisions. However, explanations come
with costs in terms of, e.g., delayed actions. Hence, it is not always obvious whether explanations will improve the satisfaction of
system goals and, if so, when to provide them to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions
under which they are warranted. Specifically, we characterize explanations in terms of their impact on a human operator's ability to effectively engage in adaptive actions. We then present a decision-making approach for planning in self-adaptation that leverages a probabilistic reasoning tool to determine when the explanation should be used in an adaptation strategy in order to improve overall system utility. We illustrate our approach in a
representative scenario for the application of an adaptive news website in the context of potential denial-of-service attacks.