Abductive Inference Models for Diagnostic Problem-Solving

1990
ISBN: 978-0-387-97343-2
Verlag: Springer

Buch, Englisch, 285 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1320 g

Reihe: Artificial Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Reggia, James A.

Peng, Yun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

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.