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Making decisions under uncertainty is a common challenge in numerous application domains, such as autonomic robotics, finance and medicine. Decision Networks are probabilistic graphical models that propose an extension of Bayesian Networks and can address the problem of Decision-Making under uncertainty. For an embedded version of Decision-Making, the related implementation must be adapted to constraints on resources, performance and power consumption. In this paper, we introduce a high-leveldoi:10.4230/oasics.asd.2019.9 dblp:conf/date/ZermaniD19 fatcat:j7inzghkjrdntmjgpbuohj7ojq