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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 G¨odel 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 |
