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IJMLC 2014 Vol.4(3): 232-236 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.417

Extending Model Checking to Efficient Propositional Inference

Guillermo de Ita Luna, Luis Polanco-Balcazar, and Omar Pérez-Barrios

Abstract—Abstract—Propositional Inference is of special concern to Artificial Intelligence, and it has a direct relationship to automatic reasoning. Given a Knowledge Base Σ and a query Φ, propositional inference is concern to determine if Φ can be logically deduced from Σ, that is, if Σ ├ Φ. We show a deterministic and a complete polynomial time algorithm for given the knowledge base Σ in Disjunctive Form and Φ in Conjunctive Form, to decide if Σ ├ Φ.

Index Terms—Automatic reasoning, efficient propositional inference, knowledge base systems.

Guillermo de Ita Luna, Luis Polanco-Balcazar, and Omar Pérez-Barrios are with the Computer Science Faculty, Autonomus University of Puebla (FCC-BUAP), Mexico (e-mail: deita@cs.buap.mx, siulpolb@outlook.com, peb.omar@hotmail.com).

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Cite: Guillermo de Ita Luna, Luis Polanco-Balcazar, and Omar Pérez-Barrios, "Extending Model Checking to Efficient Propositional Inference," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 232-236, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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