Enablers and Inhibitors in Causal Justifications of Logic Programs

  • Pedro Cabalar
  • Jorge Fandinno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9345)


In this paper we propose an extension of logic programming (LP) where each default literal derived from the well-founded model is associated 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 LP: Why-not Provenance (WnP) and Causal Graphs (CG). We show that the current approach extends both WnP and CG justifications under the Well-Founded Semantics and, as a byproduct, we also establish a formal relation between these two approaches.


Logic Programming Stable Model Double Negation Counterfactual Dependence Actual Causation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We are thankful to Carlos Damasio for his suggestions and comments on earlier versions of this work. We also thank the anonymous reviewers for their help to improve the paper. This research was partially supported by Spanish Project TIN2013-42149-P.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CorunnaA CorunnaSpain

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