Abstract?Diabetes is a chronic disease that frequently requires administration of insulin. The choice of the right amount of insulin is usually done by the patient, but both too large and too small amounts of insulin can have dire and even life threatening effects. Against this background, there is a constant trend in developing systems which help the patient or even completely take over the decision, as the artificial pancreas. Both manual and assisted therapy try to stabilize the blood glucose (BG) value around a given safe target, typically around 100 mg/dl. However, even these systems have to work on the basis of some imprecise information by the patient, in particular on the amount of carbohydrates in the meals. Much work has been done to improve this estimation and to provide better models of the insulin/glucose metabolism in spite of the natural intra-patient variation. Differently from that, this paper proposes a different framework, in which the unavoidable uncertainty is modeled in probabilistic terms and the control goal is defined not in terms of proximity to a specific BG target but as keeping the risk of leaving the euglycemic range under a given threshold. This is achieved by a Markov chain model based on BG regions. The degree of freedom gained by this problem relaxation can be used for other purposes, e.g. the minimization of total insulin intake, as shown in some in silico examples.