Alternating Fixpoint Operator for Hybrid MKNF Knowledge Bases as an Approximator of AFT. Issue 2 (1st March 2022)

• Record Type: Journal Article
• Authors: LIU, FANGFANG; YOU, JIA-HUAI
• Is Part Of: Theory and practice of logic programming. Volume 22:Issue 2(2022)
• Page Start: 305
• Page End: 334
• Publication Date: 2022-03-01
• Subjects: approximation fixpoint theory -- hybrid MKNF knowledge bases -- logic programs -- answer set semantics -- description logics -- inconsistencies; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068421000168
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: Approximation fixpoint theory (AFT) provides an algebraic framework for the study of fixpoints of operators on bilattices and has found its applications in characterizing semantics for various classes of logic programs and nonmonotonic languages. In this paper, we show one more application of this kind: the alternating fixpoint operator by Knorr et al. for the study of the well-founded semantics for hybrid minimal knowledge and negation as failure (MKNF) knowledge bases is in fact an approximator of AFT in disguise, which, thanks to the abstraction power of AFT, characterizes not only the well-founded semantics but also two-valued as well as three-valued semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved approximator for these knowledge bases, of which the least stable fixpoint is information richer than the one formulated from Knorr et al.'s construction. This leads to an improved computation for the well-founded semantics. This work is built on an extension of AFT that supports consistent as well as inconsistent pairs in the induced product bilattice, to deal with inconsistencies that arise in the context of hybrid MKNF knowledge bases. This part of the work can be considered generalizing the original AFT from symmetric approximators to arbitrary approximators.