Abstract

Model-based reasoning about a system requires an explicit representation of the system's components and their connections. Diagnosing such a system consists of locating those components whose abnormal behavior accounts for the faulty system behavior. In order to increase the efficiency of model-based diagnosis, we propose a model representation at several levels of detail, and define three refinement (abstraction) operators. We specify formal conditions that have to be satisfied by the hierarchical representation, and emphasize that the multi-level scheme is independent of any particular single-level model representation. The hierarchical diagnostic algorithm which we define turns out to be very general. We show that it emulates the bisection method, and can be used for hierarchical constraint satisfaction. We apply the hierarchical modeling principle and diagnostic algorithm to a medium-scale medical problem. The performance of a four-level qualitative model of the heart is compared t...

Citations

765	A theory of diagnosis from first principles - Reiter - 1987
637	A theory and methodology of inductive learning - Michalski - 1983
411	Planning in a hierarchy of abstraction spaces - Sacerdoti - 1974
188	Planning as Search: A Quantitative Approach - Korf - 1987
150	Computerbased Medical Consultations: MYCIN - Shortliffe - 1976
135	PROLOG: Programming for artificial intelligence - Bratko - 2001
130	The use of design descriptions in automated diagnosis - Genesereth - 1984
78	Methodology and Implementation of a CLP System - Jaffar, Michaylov - 1987
67	KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems - Bratko, Mozetic, et al. - 1989
66	Theorem proving with abstraction - Plaisted - 1981
53	General diagnosis by abductive inference - Cox, Pietrzykowski - 1987
47	Deduction revision by intelligent backtracking - Bruynooghe, Pereira - 1984
35	CLP(ℜ) and some electrical engineering problems - Heintze, Michaylov, et al. - 1987
27	Abstract theorem proving - Giunchiglia, Walsh - 1989
21	An improved constraint propagation algorithm for diagnosis - ner, Pearl - 1987
21	The induction of fault diagnosis systems from qualitative models - Pearce - 1988
19	Local Methods for Localizing Faults in Electronic Circuits - Kleer - 1976
17	Constraint satisfaction from a deductive viewpoint - Bibel - 1988
15	The role of abstractions in learning qualitative models - Mozetic - 1987
12	Preserving Consistency across Abstraction Mappings - Tenenberg - 1987
6	Reduction of diagnostic complexity through model abstractions. Report TR-90-10, Austrian Research Institute for Artificial Intelligence - Mozetic - 1990
5	Simulation-assisted inductive learning - Buchanan, Sullivan, et al. - 1988
5	Knowledge extraction through learning from examples - Mozetic - 1986
3	Varying Levels of Abstraction in Qualitative Modelling - Mozetic, Bratko, et al. - 1990
2	Extending explanation-based generalization by abstraction operators - Mozetic, Holzbaur - 1991