Deductive inference for the interiors and exteriors of horn theories

Kazuhisa Makino, Hirotaka Ono

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we investigate the deductive inference for the interiors and exteriors of Horn knowledge bases, where the interiors and exteriors were introduced by Makino and Ibaraki [11] to study stability properties of knowledge bases. We present a linear time algorithm for the deduction for the interiors and show that it is co-NP-complete for the deduction for the exteriors. Under model-based representation, we show that the deduction problem for interiors is NP-complete while the one for exteriors is co-NP-complete. As for Horn envelopes of the exteriors, we show that it is linearly solvable under model-based representation, while it is co-NP-complete under formula-based representation. We also discuss the polynomially solvable cases for all the intractable problems.

Original languageEnglish
Title of host publicationAlgorithms and Computation - 19th International Symposium, ISAAC 2008, Proceedings
Pages390-401
Number of pages12
DOIs
Publication statusPublished - Dec 1 2008
Event19th International Symposium on Algorithms and Computation, ISAAC 2008 - Gold Coast, QLD, Australia
Duration: Dec 15 2008Dec 17 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5369 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Symposium on Algorithms and Computation, ISAAC 2008
CountryAustralia
CityGold Coast, QLD
Period12/15/0812/17/08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Makino, K., & Ono, H. (2008). Deductive inference for the interiors and exteriors of horn theories. In Algorithms and Computation - 19th International Symposium, ISAAC 2008, Proceedings (pp. 390-401). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5369 LNCS). https://doi.org/10.1007/978-3-540-92182-0_36