Overview
Part of the book series: Symbolic Computation (SYMBOLIC)
Part of the book sub series: Artificial Intelligence (1064)
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About this book
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.
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Abductive Inference Models for Diagnostic Problem-Solving
Authors: Yun Peng, James A. Reggia
Series Title: Symbolic Computation
DOI: https://doi.org/10.1007/978-1-4419-8682-5
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1990
Hardcover ISBN: 978-0-387-97343-2Published: 26 June 1990
Softcover ISBN: 978-1-4612-6450-7Published: 20 November 2012
eBook ISBN: 978-1-4419-8682-5Published: 06 December 2012
Edition Number: 1
Number of Pages: XII, 285
Topics: Artificial Intelligence