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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
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.

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