A first-order logic for reasoning under uncertainty using rough sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1994-08

AUTHORS

Simon Parsons, Miroslav Kubat

ABSTRACT

Reasoning with uncertain information is a problem of key importance when dealing with knowledge from real situations. Obtaining the precise numbers required by many uncertainty-handling formalisms can be a problem when building real systems. The theory of rough sets allows us to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. The authors develop a set of symbolic truth values based upon rough sets which may be used to augment predicate logic, and provide methods for combining these truth values so that they may be propagated when augmented logic formulae are used in automated reasoning. More... »

PAGES

211-223

References to SciGraph publications

  • 1993. Representing Uncertain Knowledge, An Artificial Intelligence Approach in NONE
  • 1975. Applications of Fuzzy Sets to Systems Analysis in NONE
  • 1982-10. Rough sets in INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
  • 1985-09. Incidence calculus: A mechanism for probabilistic reasoning in JOURNAL OF AUTOMATED REASONING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00123694

    DOI

    http://dx.doi.org/10.1007/bf00123694

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018080907


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