In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. The logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper presents Possibilistic Logic Programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on Gödel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program P by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studied. Key words: Possibilistic logic, vague knowledge, defeasible argumentation, intelligent systems 1 This paper revises and extends two previous authors' papers [20] and [22] .
Alsinet, et al. REF present a new logic programming language, called Possibilistic Defeasible Logic Programming, which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty.
A Logic Programming Framework for Possibilistic Argumentation: Formalization and Logical Properties 1
Fuzzy Sets Syst.
Fuzzy Sets Syst.
Computer Science