Abstract. We introduce a special family of (assumption-based argumentation) frameworks for reasoning about the bene ts of decisions. These frameworks can be used for representing the knowledge of intelligent agents that can autonomously choose the \best" decisions, given subjective needs and preferences of decision-makers they \represent". We understand \best" decisions as dominant ones, giving more bene ts than any other decisions. Dominant decisions correspond, within the family of argumentation frameworks considered, to admissible arguments. We also propose the use of degrees of admissibility of arguments as a heuristic to assess subjectively the value of decisions and rank them from \best" (dominant) to \worst". We extend this method to provide notion of relative value of decisions where preferences over bene ts are taken into account. Finally, we show how our techniques can be successfully applied to the problem of selecting satellite images to monitor oil spills, to support electronic marketplaces for earth observation products.1 Introduction This paper presents two methods for evaluating and ranking (respectively) decisions using assumption-based argumentation (ABA) frameworks [4,13,14]. These ABA frameworks can be used for representing the knowledge of intelligent agents that autonomously make the best decision, e.g. by choosing the \best" items/ services available in a service-oriented architecture, e.g. as envisaged in [32]. We de ne a notion of dominance to characterise \best" items, and show that these items are those that correspond, within the family of argumentation frameworks considered, to admissible arguments as de ned in [4,13]. Intuitively, the method relies upon comparing the value of di erent items by \arguing" about the bene ts they provide. The reason why an item provides a bene t is explained logically (by means of arguments). ABA allows to study collections of logically constructed arguments based upon assumptions. As we shall see, ABA frameworks are particularly adequate for representing and reasoning about the bene ts of items as well as for providing explanations to users. In practice, decision-makers do not always choose top-quality (\best") items, but often choose items which t their budget and have highest quality/price ratio. In this context, it is useful to assess numerically the quality or relative quality of items. We propose a novel criterion (or \semantics", as understood in argumentation) for choosing sets of assumptions and arguments. This criterion