Abductive Inference Models for Diagnostic Problem-Solving
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Abductive Inference Models for Diagnostic Problem-Solving “
- Abduction and Diagnostic Inference- Computational Models for Diagnostic Problem Solving
- Basics of Parsimonious Covering Theory
- Probabilistic Causal Model
- Diagnostic Strategies in the Probabilistic Causal Model
- Causal Chaining
- Parallel Processing for Diagnostic Problem-Solving
- Conclusion
- Bibliography
- Index
Bibliographische Angaben
- Autoren: Yun Peng , James A. Reggia
- 1990, 1990., 285 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, New York
- ISBN-10: 0387973435
- ISBN-13: 9780387973432
- Erscheinungsdatum: 26.06.1990
Sprache:
Englisch
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