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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, 2018. Accepted for publication.

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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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