Argumentation-based abduction in disjunctive logic programming

In this paper, we propose an argumentation-based semantic framework, called DAS, for disjunctive logic programming. The basic idea is to translate a d isjunctive logic program into an argumentation-theoretic framework. One uni que feature of our proposed framework is to consider the disjunctions of ne gative literals as possible assumptions so as to represent incomplete infor mation. In our framework, three semantics preferred disjunctive hypothesis (PDH), complete disjunctive hypothesis (CDH) and well-founded disjunctive h ypothesis (WFDH) are defined by three kinds of acceptable hypotheses to rep resent credulous, moderate and skeptical reasoning in artificial intelligen ce (AI), respectively. Furthermore, our semantic framework can be extended to a wider class than that of disjunctive programs (called bi-disjunctive l ogic programs). In addition to being a first serious attempt in establishin g an argumentation-theoretic framework for disjunctive logic programming, D AS integrates and naturally extends many key semantics, such as the minimal models, extended generalized closed world assumption (EGCWA), the well-fou nded model, and the disjunctive stable models. In particular, novel and int eresting argumentation-theoretic characterizations of the EGCWA and the dis junctive stable semantics are shown. Thus the framework presented in this p aper does not only provide a new way of performing argumentation (abduction ) in disjunctive deductive databases, but also is a simple, intuitive and u nifying semantic framework for disjunctive logic programming. (C) 2000 Else vier Science Inc. All rights reserved.