COUPLED PROBABILITY-POSSIBILITY METHOD FOR DECISION-MAKING IN KNOWLEDGE-BASED SYSTEMS.

Magdi Ragheb, L. Tsoukalas

Research output: Contribution to conferencePaper

Abstract

A methodology is proposed for inferencing and decision-making under uncertainty in production-rule analysis systems where the knowledge about a given system is both probabilistic and possibilistic. In this approach, uncertainty is considered as consisting of two parts: Randomness, meaning the uncertainty of occurrence of an object, and Fuzziness, expressing the imprecision of the meaning of the object. The analysis uses the concepts of the probability of a fuzzy event and of information granularity. Application to the monitoring of engineering devices is demonstrated, where the propagation of the coupled probabilistic and possibilistic uncertainty is carried out over a model-based system using the Knowledge Engineering Rule-Based paradigm. The approach provides a quantification for combining the concepts of 'Reliability' from probability theory, and of 'Performance Level' from the theory of fuzzy subsets.

Original languageEnglish (US)
Pages97-105
Number of pages9
StatePublished - Dec 1 1986

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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