Horn formulae are widely used in different settings that include logic programming, answer set programming, description logics, deductive databases, and system verification, among many others. One concrete example is concept subsumption in lightweight description logics, which can be reduced to inference in propositional Horn formulae. Some problems require one to reason with inconsistent Horn formulae. This is the case when providing minimal explanations of inconsistency. This paper proposes efficient algorithms for a number of decision, function and enumeration problems related with inconsistent Horn formulae. Concretely, the paper develops efficient algorithms for finding and enumerating minimal unsatisfiable subsets (MUSes), minimal correction subsets (MCSes), but also for computing the lean kernel. The paper also shows the practical importance of some of the proposed algorithms.

João, M., Alexey, I., Carlos, M., PENALOZA NYSSEN, R. (2016). Efficient reasoning for inconsistent horn formulae. In LOGICS IN ARTIFICIAL INTELLIGENCE, (JELIA 2016) (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.336-352). Springer Verlag [10.1007/978-3-319-48758-8_22].

Efficient reasoning for inconsistent horn formulae

Rafael Peñaloza
2016

Abstract

Horn formulae are widely used in different settings that include logic programming, answer set programming, description logics, deductive databases, and system verification, among many others. One concrete example is concept subsumption in lightweight description logics, which can be reduced to inference in propositional Horn formulae. Some problems require one to reason with inconsistent Horn formulae. This is the case when providing minimal explanations of inconsistency. This paper proposes efficient algorithms for a number of decision, function and enumeration problems related with inconsistent Horn formulae. Concretely, the paper develops efficient algorithms for finding and enumerating minimal unsatisfiable subsets (MUSes), minimal correction subsets (MCSes), but also for computing the lean kernel. The paper also shows the practical importance of some of the proposed algorithms.
paper
inconsistency;
English
15th European Conference on Logics in Artificial Intelligence, JELIA 2016 9-11 November
2016
Michael, L; Kakas, A
LOGICS IN ARTIFICIAL INTELLIGENCE, (JELIA 2016) (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-331948757-1
2016
10021
336
352
none
João, M., Alexey, I., Carlos, M., PENALOZA NYSSEN, R. (2016). Efficient reasoning for inconsistent horn formulae. In LOGICS IN ARTIFICIAL INTELLIGENCE, (JELIA 2016) (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.336-352). Springer Verlag [10.1007/978-3-319-48758-8_22].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/303184
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