A rough set approach to reasoning under uncertainty

Simon Parsons, Miroslav Kubat, Mirko Dohnal

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Reasoning with uncertain information is a problem of key importance when dealing with information about the real world. Obtaining the precise numbers required by many uncertainty handling formalisms can be a problem. The theory of rough sets makes it possible to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. This paper presents an introduction to various forms of reasoning under uncertainty that are based on rough sets. In particular, a number of sets of numerical and symbolic truth values which may be used to augment propositional logic are developed, and a semantics for these values is provided based upon the notion of possible worlds. Methods of combining the truth values are developed so that they may be propagated when augmented logic formulae are combined, and their use is demonstrated in theorem proving.

Original languageEnglish (US)
Pages (from-to)175-193
Number of pages19
JournalJournal of Experimental and Theoretical Artificial Intelligence
Volume7
Issue number2
DOIs
StatePublished - Jan 1 1995
Externally publishedYes

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Keywords

  • Automated reasoning
  • Evidence theory
  • Propositional logic
  • Rough sets
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence

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