ABDUCTIVE REASONING BY CONSTRUCTING PROBABILISTIC DEDUCTION GRAPHS FOR SOLVING THE DIAGNOSIS PROBLEM

被引：2

作者：

LI, HL

YANG, CC

机构：

[1] Institute of Information Management, National Chiao-Tung University, Hsinchu

[2] Department of Computer Sciences, University of North Texas, Denton, TX

来源：

DECISION SUPPORT SYSTEMS | 1991年 / 7卷 / 02期

关键词：

ABDUCTION; CAUSAL NETWORK; DEDUCTION; DEDUCTION GRAPH; DIAGNOSIS; EXPERT SYSTEM; INTEGER PROGRAMMING; MUTUALLY INDEPENDENT OR EXCLUSIVE; OPTIMIZATION; PROBABILISTIC REASONING;

D O I：

10.1016/0167-9236(91)90051-C

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An algorithm is proposed for finding optimal solutions of the diagnosis problem by using deduction graphs (DG) to accomplish abductions of multiple causes and multiple symptoms. The relationship among causes, symptoms, and possible intermediaries is represented by a causal network. The algorithm accomplishes the abduction by constructing a deduction graph DG(C,S) from the cause set C to the symptom set S representing the subnetwork such that the product of the prior probability, P(C), of C and the conditional probability, P(S\C), of DG(C,S) is maximized. An optimal solution is achieved by solving a 0/1 linear integer programming problem. Based on some assumptions, the algorithm can deal with a causal network involving various mutually independent deduction graphs.

引用

页码：121 / 131

页数：11

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