A minimal connection model of abductive diagnostic reasoning

Author

Lin, Dekang ; Goebel, Randy

Author_Institution

Dept. of Comput. Sci., Alberta Univ., Edmonton, Alta., Canada

fYear

1990

fDate

5-9 May 1990

Firstpage

16

Abstract

A minimal connection model of abductive diagnostic reasoning is presented. The domain knowledge is represented by a causal network. An explanation of a set of observations is a chain of causation events. These causation events constitute a scenario where all the observations can be observed. The authors define the best explanation to be the most probable explanation. The underlying causal model enables one to compute the probabilities of explanations from the conditional probabilities of the participating causation events. An algorithm for finding the most probable explanations is presented. Although probabilistic inference using belief networks is NP-hard in general, this algorithm is polynomial to the number of nodes in the networks and is exponential only to the number of observations to be explained, which, in any single case, is usually small

Keywords

computational complexity; explanation; inference mechanisms; knowledge representation; probability; abductive diagnostic reasoning; belief networks; causal network; causation events; domain knowledge; minimal connection model; most probable explanations; nodes; observations; polynomial complexity; probabilistic inference; Diagnostic expert systems; Fault diagnosis; Inference algorithms; Polynomials; Probability distribution; Proposals;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence Applications, 1990., Sixth Conference on

Conference_Location

Santa Barbara, CA

Print_ISBN

0-8186-2032-3

Type

conf

DOI

10.1109/CAIA.1990.89166

Filename

89166