Abductive Inference Models for Diagnostic Problem-Solving

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.

Sommario

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.

Dettagli sul prodotto

• Autori: Yu Peng, Yun Peng, James A Reggia, James A. Reggia
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 17.10.2013
• EAN: 9781461264507
• ISBN: 978-1-4612-6450-7
• Pagine: 285
• Dimensioni: 155 mm x 234 mm x 16 mm
• Peso: 476 g
• Illustrazioni: XII, 285 p.
• Serie: Artificial Intelligence; Symbolic computation/Artificial Intelligence; Symbolic Computation; Artificial Intelligence; Symbolic Computation
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica