Détail de la notice
Titre du Document
Probabilistic reasoning based on dynamic causality trees/diagrams
Auteur(s)
QIN ZHANG
Résumé
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
Editeur
Elsevier
Identifiant
ISSN : 0951-8320
Source
Reliability engineering & systems safety A. 1994, vol. 46, n° 3, pp. 209-220 [bibl. : 17 ref.]
Langue
Anglais
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