@InProceedings{2021:Li:HeyPreparatory,
AUTHOR = {Li, Nianyu and C\'{a}mara, Javier and Garlan, David and Schmerl, Bradley and Jin, Zhi},
TITLE = {Hey! Preparing Humans to do Tasks in Self-adaptive Systems},
YEAR = {2021},
MONTH = {18-21 May},
BOOKTITLE = {Proceedings of the 16th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2021)},
ADDRESS = {Virtual},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/Preparatory_SEAMS.pdf},
ABSTRACT = {Many self-adaptive systems benefit from human involvement, where human operators can complement the capabilities of systems (e.g., by supervising decisions, or performing adaptations and tasks involving physical changes that cannot be automated). However, insufficient preparation (e.g., lack of task context comprehension) may hinder the effectiveness of human involvement, especially when operators are unexpectedly interrupted to perform a new task. Preparatory notification of a task provided in advance can sometimes help human operators focus their attention on the forthcoming task and understand its context before task execution, hence improving effectiveness. Nevertheless, deciding when to use preparatory notification as a tactic is not obvious and entails considering different factors that include uncertainties induced by human operator behavior (who might ignore the notice message), human attributes (e.g., operator training level), and other information that refers to the state of the system and its environment.
In this paper, informed by work in cognitive science on human attention and context management, we introduce a formal framework to reason about
the usage of preparatory notifications
in self-adaptive systems involving human operators. Our framework characterizes the effects of managing attention via task notification in terms of task context comprehension. We also build on our framework to develop an automated probabilistic reasoning technique able to determine when and in what form a preparatory notification tactic should be used to optimize system goals. We illustrate our approach in a representative scenario of human-robot collaborative goods delivery. },
NOTE = {Awarded Best Student Paper for SEAMS 2021},
KEYWORDS = {Explainable Software, Formal Methods, Robot Adaptation, Self-adaptation}
}
|
|
