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Titre du document / Document title

Probabilistic reasoning based on dynamic causality trees/diagrams

Auteur(s) / Author(s)

QIN ZHANG ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

Technological Innovation Corp. China, Beijing 100036, CHINE

Résumé / Abstract

The techniques of artificial intelligence have been widely used in many areas, including reliability engineering and system safety. e.g. the expert systems for fault diagnoses of complex engineering systems. Uncertainties are an important issue to be addressed in these techniques. This paper presents a methodology dealing with the probabilistic reasoning under uncertainty in artificial intelligence systems. This methodology is based on the newly defined causality trees/diagrams that can be either singly or multiply connected: moreover, it can include causality loops. Two new kinds of events, basic events and linkage events, are introduced. Their probabilities of occurrence are easily obtained from subjective belief or statistics, and are independent of each other. Thus, they are modular and deliverable as a part of knowledge. Also, the causality trees/diagrams can include on-line dynamical information. Two equivalent belief updating approaches are presented which operate regardless of whether the target system is singly connected, multiply connected or causally looped. Two examples are given to illustrate and prove this methodology

Revue / Journal Title

Reliability engineering & systems safety    ISSN  0951-8320 

Source / Source

1994, vol. 46, no3, pp. 209-220 (17 ref.)

Langue / Language

Anglais

Editeur / Publisher

Elsevier, Oxford, ROYAUME-UNI  (1988) (Revue)

Mots-clés anglais / English Keywords

Expert system

;

Error diagnostic

;

Methodology

;

Causality

;

Reasoning

;

Probabilistic approach

;

Probabilistic reasoning

;

Mots-clés français / French Keywords

Système expert

;

Diagnostic erreur

;

Méthodologie

;

Causalité

;

Raisonnement

;

Approche probabiliste

;

Raisonnement probabiliste

;

Mots-clés espagnols / Spanish Keywords

Sistema experto

;

Diagnóstico error

;

Metodología

;

Causalidad

;

Razonamiento

;

Aproximación probabilista

;

Localisation / Location

INIST-CNRS, Cote INIST : 19321, 35400005963443.0020

Nº notice refdoc (ud4) : 3486167



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