Runtime models based on dynamic decision networks: Enhancing the decision-making in the domain of ambient assisted living applications

Dynamic decision-making for self-Adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -Aka quality properties-And the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-Time. In this paper we develop further our ideas on re-Assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage.