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Inference Algorithm with Falsifying Patterns for Belief Revision

  • P. BelloEmail author
  • G. De Ita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)

Abstract

Let K be a Knowledge Base (KB) and let \(\phi \) be a query, the entailment problem \(K \models \phi \) is crucial for solving the Belief Revision problem. The Belief Revision problem consists in incorporate new beliefs into knowledge base already established, changing as little as possible the original beliefs and maintaining consistency of the KB. A widely accepted framework for reasoning through intelligent systems is the knowledge-based system approach. The general idea is to keep the knowledge in some representative language with a well-defined connotation, particularly it we will be used prepositional logic for modelling the knowledge base and the new information.

This article shows that the use of falsifying patterns for expressing clauses help to determine whether an conjunctive normal form (CNF) is inferred from another CNF, and therefore, it allows us to construct an algorithm for belief revision between CNF’s.

Our algorithm applies a depth first search in order to obtain an effective process for the belief revision between the conjuntive forms K and \(\phi \).

Keywords

Belief revision Propositional inference Falsifying patterns 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Language & Knowledge Engineering Lab. (LKE), Faculty of Computer ScienceBenemérita Universidad Autónoma de PueblaPueblaMexico

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