Allowing cyclic dependencies in modular logic programming

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Abstract

Even though modularity has been studied extensively in conventional logic programming, there are few approaches on how to incorporate modularity into Answer Set Programming, a prominent rule-based declarative programming paradigm. A major approach is Oikarinnen and Janhunen’s Gaifman-Shapiro-style architecture of program modules, which provides the composition of program modules. Their module theorem properly strengthens Lifschitz and Turner’s splitting set theorem for normal logic programs. However, this approach is limited by module conditions that are imposed in order to ensure the compatibility of their module system with the stable model semantics, namely forcing output signatures of composing modules to be disjoint and disallowing positive cyclic dependencies between different modules. These conditions turn out to be too restrictive in practice and after recently discussing alternative ways of lifting the first restriction [17], we now show how one can allow positive cyclic dependencies between modules, thus widening the applicability of this framework and the scope of the module theorem.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PublisherSpringer Verlag
Pages363-375
Number of pages13
Volume9273
ISBN (Electronic)978-3-319-23485-4
DOIs
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sept 201511 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9273
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015
Country/TerritoryPortugal
CityCoimbra
Period8/09/1511/09/15

Keywords

  • Computer Science
  • Artificial Intelligence
  • Computer Science, Theory & Methods

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