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@InProceedings{Camara/2015/Human,
AUTHOR = {C\'{a}mara, Javier and Moreno, Gabriel A. and Garlan, David},
TITLE = {Reasoning about Human Participation in Self-Adaptive Systems},
YEAR = {2015},
MONTH = {18-19 May},
BOOKTITLE = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015)},
ADDRESS = {Florence, Italy},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/seams15.pdf},
ABSTRACT = {Self-adaptive systems overcome many of the limitations of human supervision in complex software-intensive systems
by endowing them with the ability to automatically adapt their
structure and behavior in the presence of runtime changes.
However, adaptation in some classes of systems (e.g., safety-
critical) can benefit by receiving information from humans (e.g.,
acting as sophisticated sensors, decision-makers), or by involving
them as system-level effectors to execute adaptations (e.g., when
automation is not possible, or as a fallback mechanism). However,
human participants are influenced by factors external to the
system (e.g., training level, fatigue) that affect the likelihood
of success when they perform a task, its duration, or even if
they are willing to perform it in the first place. Without careful
consideration of these factors, it is unclear how to decide when to
involve humans in adaptation, and in which way. In this paper,
we investigate how the explicit modeling of human participants
can provide a better insight into the trade-offs of involving
humans in adaptation. We contribute a formal framework to
reason about human involvement in self-adaptation, focusing on
the role of human participants as actors (i.e., effectors) during
the execution stage of adaptation. The approach consists of:
(i) a language to express adaptation models that capture factors
affecting human behavior and its interactions with the system,
and (ii) a formalization of these adaptation models as stochastic
multiplayer games (SMGs) that can be used to analyze human-
system-environment interactions. We illustrate our approach in
an adaptive industrial middleware used to monitor and manage
sensor networks in renewable energy production plants.},
KEYWORDS = {Assurance, Human-in-the-loop, Self-adaptation, Self-awareness & Adaptation, Stochastic Games}
}
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