Enablers and inhibitors in causal justifications of logic programs*. Issue 1 (3rd May 2016)

• Record Type: Journal Article
• Authors: CABALAR, PEDRO; FANDINNO, JORGE
• Is Part Of: Theory and practice of logic programming. Volume 17:Issue 1(2017)
• Page Start: 49
• Page End: 74
• Publication Date: 2016-05-03
• Subjects: causal justifications, -- well-founded semantics, -- stable models, -- answer set programming; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068416000107
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: In this paper, we propose an extension of logic programming where each default literal derived from the well-founded model is associated to a justification represented as an algebraic expression. This expression contains both causal explanations (in the form of proof graphs built with rule labels) and terms under the scope of negation that stand for conditions that enable or disable the application of causal rules. Using some examples, we discuss how these new conditions, we respectively call enablers and inhibitors, are intimately related to default negation and have an essentially different nature from regular cause-effect relations. The most important result is a formal comparison to the recent algebraic approaches for justifications in logic programming: Why-not Provenance and Causal Graphs . We show that the current approach extends both Why-not Provenance and Causal Graphs justifications under the well-founded semantics and, as a byproduct, we also establish a formal relation between these two approaches.