Computing Abductive Argumentation in Answer Set Programming

In our daily life, humans often argue with each other using abductive knowledge which includes not only facts known to be true but also hypotheses that may be expected to be true. This paper presents a novel approach to find out every skeptical (resp. credulous) explanation which is the set of hypotheses needed to skeptically (resp. credulously) justify the argument supporting a disputer's claim based on abductive knowledge base under the specified argumentation semantics. The main subject of this paper is the definition of the Abductive Argumentation Framework which is equivalent to the widely adopted Dung's framework except handling hypotheses, and from which skeptical (resp. credulous) explanations in argumentation can be defined. In general, there are multiple explanations under the specified argumentation semantics. Our approach is capable of finding out all of them by means of applying traditional abductive logic programming to our previous work of computing argumentation semantics in answer set programming (ASP). Thus this study eventually reveals the greatest advantage of applying ASP to the crucial decision problems in the research field of argumentation.