Reasoning about Sensing Uncertainty and its Reduction in Decision-Making for Self-Adaptation
Javier Cámara, Wenxin Peng,
David Garlan and
Bradley Schmerl.
In Science of Computer Programming, Vol. 167:51-69, 1 December 2018.
Online links:   Plain Text
Abstract
| Adaptive systems are expected to adapt to unanticipated run-time events using imperfect information about themselves, their environment, and goals. This entails
handling the effects of uncertainties in decision-making, which are not always
considered as a first-class concern. This paper contributes a formal analysis technique that explicitly considers uncertainty in sensing when reasoning about the
best way to adapt, together with uncertainty reduction mechanisms to improve
system utility. We illustrate our approach on a Denial of Service (DoS) attack
scenario and present results that demonstrate the benefits of uncertainty-aware
decision-making in comparison to using an uncertainty-ignorant approach, both
in the presence and absence of uncertainty reduction mechanisms. |
Keywords: Formal Methods, Self-adaptation.
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