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

Simon Parsons, Miroslav Kubat

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)211-223
Number of pages13
JournalJournal of Intelligent Manufacturing
Volume5
Issue number4
DOIs
StatePublished - Aug 1 1994
Externally publishedYes

Fingerprint

Uncertainty

Keywords

  • possible worlds
  • principle
  • resolution
  • rough sets
  • rules of inference
  • theorem proving
  • Uncertainty

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Cite this

A first-order logic for reasoning under uncertainty using rough sets. / Parsons, Simon; Kubat, Miroslav.

In: Journal of Intelligent Manufacturing, Vol. 5, No. 4, 01.08.1994, p. 211-223.

Research output: Contribution to journalArticle

@article{0015a1d1e5b94d2cb492a950b333e4ad,
title = "A first-order logic for reasoning under uncertainty using rough sets",
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.",
keywords = "possible worlds, principle, resolution, rough sets, rules of inference, theorem proving, Uncertainty",
author = "Simon Parsons and Miroslav Kubat",
year = "1994",
month = "8",
day = "1",
doi = "10.1007/BF00123694",
language = "English",
volume = "5",
pages = "211--223",
journal = "Journal of Intelligent Manufacturing",
issn = "0956-5515",
publisher = "Springer Netherlands",
number = "4",

}

TY - JOUR

T1 - A first-order logic for reasoning under uncertainty using rough sets

AU - Parsons, Simon

AU - Kubat, Miroslav

PY - 1994/8/1

Y1 - 1994/8/1

N2 - 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.

AB - 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.

KW - possible worlds

KW - principle

KW - resolution

KW - rough sets

KW - rules of inference

KW - theorem proving

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=12944259480&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=12944259480&partnerID=8YFLogxK

U2 - 10.1007/BF00123694

DO - 10.1007/BF00123694

M3 - Article

VL - 5

SP - 211

EP - 223

JO - Journal of Intelligent Manufacturing

JF - Journal of Intelligent Manufacturing

SN - 0956-5515

IS - 4

ER -