Real-world databases are often inconsistent. Although there has been an extensive body of
work on handling inconsistency, little work has been done on measuring inconsistency in …

Handling conflicting information is an important challenge in AI. Measuring inconsistency is
an approach that provides ways to quantify the severity of inconsistency and helps …

AI systems often need to deal with inconsistent information. For this reason, since the early
2000s, some AI researchers have developed ways to measure the amount of inconsistency …

Abstract Answer Set Programming (ASP) is a prominent modeling and solving framework.
An inconsistent core (IC) of an ASP program is an inconsistent subset of rules. In the case of …

Real-world data are often inconsistent. Although a substantial amount of research has been
done on measuring inconsistency, this research concentrated on knowledge bases …

Branching time logics have been studied in computer science since the 1980s primarily to
model the tree of computations for discrete transition systems. Inconsistency measures for …

Computing small (subset-minimal or smallest) explanations is a computationally challenging
task for various logics and non-monotonic formalisms. Arguably the most progress in …

In the setting of Defeasible Logic, we deal with the problem of revising and contracting a non-
monotonic theory while minimizing the number of rules to be removed from the theory itself …

Knowledge Measures (KMs) aim at quantifying the amount of knowledge/information that a
knowledge base carries. On the other hand, Belief Change (BC) is the process of changing …

Inconsistency measures for propositional logic have been investigated in great detail for the
past 20 years. Many such measures have been proposed. Rationality postulates, conditions …