TY - JOUR
T1 - A reconstruction of multipreference closure
AU - Giordano, Laura
AU - Gliozzi, Valentina
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - The paper describes a preferential approach for dealing with exceptions in KLM preferential logics, based on the rational closure. It is well known that rational closure does not allow an independent handling of inheritance of different defeasible properties of concepts. In this work, we consider an alternative closure construction, called Multi Preference closure (MP-closure), which has been first considered for reasoning with exceptions in DLs. We reconstruct the notion of MP-closure in the propositional case and show that it is a natural (weaker) variant of Lehmann's lexicographic closure, which appears to be too bold in some cases. The MP-closure defines a preferential consequence relation that, although weaker than lexicographic closure, is stronger than Relevant Closure.
AB - The paper describes a preferential approach for dealing with exceptions in KLM preferential logics, based on the rational closure. It is well known that rational closure does not allow an independent handling of inheritance of different defeasible properties of concepts. In this work, we consider an alternative closure construction, called Multi Preference closure (MP-closure), which has been first considered for reasoning with exceptions in DLs. We reconstruct the notion of MP-closure in the propositional case and show that it is a natural (weaker) variant of Lehmann's lexicographic closure, which appears to be too bold in some cases. The MP-closure defines a preferential consequence relation that, although weaker than lexicographic closure, is stronger than Relevant Closure.
KW - Knowledge representation
KW - Nonmonotonic reasoning
KW - Preferential semantics
KW - Rational closure
UR - http://www.scopus.com/inward/record.url?scp=85093652889&partnerID=8YFLogxK
U2 - 10.1016/j.artint.2020.103398
DO - 10.1016/j.artint.2020.103398
M3 - Article
SN - 0004-3702
VL - 290
JO - Artificial Intelligence
JF - Artificial Intelligence
M1 - 103398
ER -