Partial abductive inference in Bayesian belief networks using a genetic algorithm

被引：23

作者：

de Campos, LM

Gámez, JA ^{[1
]}

Moral, S

机构：

[1] Univ Castilla La Mancha, Escuela Politecn Super, Dept Informat, Albacete 02071, Spain

[2] Univ Granada, Escuela Tecn Super Ingn Informat, Dept Ciencias Comp & Inteligencia Artificial, E-18071 Granada, Spain

来源：

PATTERN RECOGNITION LETTERS | 1999年 / 20卷 / 11-13期

关键词：

abductive inference; most probable explanation; Bayesian belief networks; genetic algorithms; probabilistic reasoning;

D O I：

10.1016/S0167-8655(99)00088-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Abductive inference in Bayesian belief networks is the process of generating the K most probable configurations given an observed evidence. When we are only interested in a subset of the network's variables, this problem is called partial abductive inference. Both problems are NP-hard, and so exact computation is not always possible. This paper describes an approximate method based on genetic algorithms to perform partial abductive inference. We have tested the algorithm using the alarm network and from the experimental results we can conclude that the algorithm presented here is a good tool to perform this kind of probabilistic reasoning. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：1211 / 1217

页数：7

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