Abductive Inference Models for Diagnostic Problem-Solving / Edition 1

by Yun Peng, James A. Reggia

Overview

Making a diagnosis when something goes wrong with a natural or m- made system can be difficult. In many fields, such as medicine or electr- ics, a long training period and apprenticeship are required to become a skilled diagnostician. During this time a novice diagnostician is asked to assimilate a large amount of knowledge about the class of systems to be diagnosed. In contrast, the novice is not really taught how to reason with this knowledge in arriving at a conclusion or a diagnosis, except perhaps implicitly through ease examples. This would seem to indicate that many of the essential aspects of diagnostic reasoning are a type of intuiti- based, common sense reasoning. More precisely, diagnostic reasoning can be classified as a type of inf- ence known as abductive reasoning or abduction. Abduction is defined to be a process of generating a plausible explanation for a given set of obs- vations or facts. Although mentioned in Aristotle's work, the study of f- mal aspects of abduction did not really start until about a century ago.

Product Details

ISBN-13:	9780387973432
Publisher:	Springer New York
Publication date:	06/26/1990
Series:	Symbolic Computation
Edition description:	1990
Pages:	285
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 Abduction and Diagnostic Inference.- 2 Computational Models for Diagnostic Problem Solving.- 3 Basics of Parsimonious Covering Theory.- 4 Probabilistic Causal Model.- 5 Diagnostic Strategies in the Probabilistic Causal Model.- 6 Causal Chaining.- 7 Parallel Processing for Diagnostic Problem-Solving.- 8 Conclusion.

Editorial Reviews

Introduces a step toward the formalization of abductive inference: a mathematical model of diagnostic reasoning the authors call parsimonious covering theory. Intended for readers with a background in artificial intelligence or cognitive science, and assumes a basic knowledge of elementary set theory, logic, and probability theory. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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