Knowledge Consistency

Knowledge consistency is the property that the database contain no contradictions. It is extremely important for knowledge representations that may only either assert or deny statements, with no measure of partial belief. One such system is first order predicate calculus. Because all statements maybe either true or false, it may be possible to store only part of the statements (a basis set), from which all true statements (or all false statements) may be derived. Statements that can't be derived from the basis set are assumed false (or true). This is the closed-world assumption. The advantages of knowledge consistency are:

  1. ability to store less statements (using closed-world assumption)

An architecture that tolerates knowledge inconsistency generally treats its knowledge base as a set of competing hypotheses, or as a set of statements that it has varying amounts of confidence in. Often there is a numerical measure of belief. This technique is used in control knowledge too: rules may be graded by how well they perform. The advantages of tolerating inconsistent knowledge are:

  1. increased flexibility in representation,
  2. increased flexibility in learning and reasoning

A related property is learning monotonicity, which is whether an architecture may learn things that contradict what it already knows. If an architecture must maintain a consistent knowledge base then any learning strategy it uses must be monotonic. 


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 Examples of consistent knowledge architectures are:



  • Atlantis by E. Gat.

  •  Prodigy by Carbonell et al

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     Examples of inconsistent knowledge architectures are:
    


  • MAX by D. Kuokka.

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Other Properties.

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