We present a new approach to representation and acquisition of normative information for machine ethics. It combines an influential philosophical account of the fundamental structure of morality with argumentation theory and machine learning. According to the philosophical account, the deontic status of an action -- whether it is required, forbidden, or permissible -- is determined through the interaction of "normative reasons" of varying strengths or weights. We first provide a formal characterization of this account, by modeling it in (weighted) argumentation graphs. We then use it to model ethical learning: the basic idea is to use a set of cases for which deontic statuses are known to estimate the weights of normative reasons in operation in these cases, and to use these weight estimates to determine the deontic statuses of actions in new cases. The result is an approach that has the advantages of both bottom-up and top-down approaches to machine ethics: normative information is acquired through the interaction with training data, and its meaning is clear. We also report the results of some initial experiments with the model.