Belief Revision in Structured Probabilistic Argumentation

PAULO SHAKARIAN; GERARDO I. SIMARI; MARCELO A. FALAPPA

LECTURE NOTES IN COMPUTER SCIENCE

Springer

Año: 2014 vol. 8367 p. 324 - 343

0302-9743

In real-world applications, knowledge bases consisting of allthe information at hand for a specific domain, along with the currentstate of affairs, are bound to contain contradictory data coming fromdifferent sources, as well as data with varying degrees of uncertaintyattached. Likewise, an important aspect of the effort associated withmaintaining knowledge bases is deciding what information is no longeruseful; pieces of information (such as intelligence reports) may be outdated,may come from sources that have recently been discovered to beof low quality, or abundant evidence may be available that contradictsthem. In this paper, we propose a probabilistic structured argumentationframework that arises from the extension of Presumptive Defeasible LogicProgramming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic Pre-DeLP programs. We propose a set of rationality postulates - based onwell-known ones developed for classical knowledge bases - that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates.