Explaining Best Decisions via Argumentation

This paper presents an argumentation-based multi-attribute decision making model, where decisions made can be explained in natural language. More specifically, an explanation for a decision is obtained from a mapping between the given decision framework and an argumentation framework, such that best decisions correspond to admissible sets of arguments, and the explanation is generated automatically from dispute trees sanctioning the admissibility of arguments. We deploy a notion of rationality where best decisions meet most goals and exhibit fewest redundant attributes. We illustrate our method by a legal example, where decisions amount to past cases most similar to a given new, open case.