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Proceedings Paper

Hypothetico-deductive diagnoses
Author(s): Pierre Marquis
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Paper Abstract

A diagnostic is inference to the state of a system from its observed and its expected behaviors. When no complete description is available--and this is not out of the ordinary in real-world applications--diagnosing a system cannot be done in a pure deductive way. To be more specific, deduction allows us to derive only partial diagnoses that must be completed to get closer to the actual one. Subsequently, searching for better diagnoses requires hypothetical reasoning, where the assumptions to be generated aim at reflecting the diagnostician beliefs. In the frame of hypothetico-deductive diagnostic, several approaches have been pointed out so far. The consistency-based method is the simplest one. It sanctions the lack of evidence that a component of a system fails by jumping to the conclusion that this component behaves correctly. In contrast to the consistency-based approach, the circumscription-based and the deductive/abductive methods take into account how components behave to complete what is deductively generated. This paper is devoted to a comparison of the consistency-based, the circumscription-based, and the deductive/abductive approaches to diagnostic. Its expected purpose is to provide a deeper understanding of both techniques. It is organized as follows: problem formulation and terminology are introduced in Section 2; Section 3 proposes a brief overview of the consistency-based, the circumscription-based, and the deductive/abductive methods; Section 4 details and compares the preference criteria each approach supports; Section 5 illustrates this comparison on a simple example; and Section 6 concludes this paper.

Paper Details

Date Published: 1 March 1992
PDF: 11 pages
Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); doi: 10.1117/12.56875
Show Author Affiliations
Pierre Marquis, CRIN-CNRS and INRIA/Lorraine (France)

Published in SPIE Proceedings Vol. 1707:
Applications of Artificial Intelligence X: Knowledge-Based Systems
Gautam Biswas, Editor(s)

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