Abductive Inference Models for Diagnostic Problem-Solving by Yun Peng
By Yun Peng
Making a analysis while whatever is going improper with a usual or m- made process may be tough. in lots of fields, corresponding to drugs or electr- ics, a protracted education interval and apprenticeship are required to turn into a talented diagnostician. in this time a amateur diagnostician is requested to assimilate a large number of wisdom in regards to the classification of platforms to be clinically determined. by contrast, the beginner seriously isn't taught the way to cause with this data in arriving at a end or a analysis, other than maybe implicitly via ease examples. this might appear to point out that the various crucial facets of diagnostic reasoning are a kind of intuiti- dependent, good judgment reasoning. extra accurately, diagnostic reasoning will be categorised as one of those inf- ence often called abductive reasoning or abduction. Abduction is outlined to be a strategy of producing a believable reason for a given set of obs- vations or evidence. even supposing pointed out in Aristotle's paintings, the examine of f- mal features of abduction didn't fairly begin until eventually a few century ago.
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Extra resources for Abductive Inference Models for Diagnostic Problem-Solving
INTERNIST-1 [Pople75], PIP [Pauker76], and IDT [Shubin82]. Their knowledge representation is tailored to specific applications, and their inference mechanisms rely heavily on heuristics extracted from the characteristics of these specific domains. Being successful in their respective applications, these domainspecific models helped to clarify the basic ideas of the abductive nature of diagnostic inference and inspired a departure from the more traditional statistical pattern classification and rule-based approaches.
Previous work on methods for answer justification can be divided into various categories [Reggia85d]. , the procedure that was followed, calculations performed, or saying that deduction was used) and/or the application-specific knowledge that was applied by the program in making decisions. Examples of programs adopting this approach include those which cite a procedurally oriented goal stack [Winograd73], state a procedure followed to accomplish a task [Swartout77], analyze the probabilities of clinical associations in a statistically oriented knowledge base [Reggia85e], and maintain a trace of the chain of deductions made during problem-solving so that appropriate rules can be produced [Davis76].
Disorders, indicated by nodes in set D, are causally related to intermediate pathological states (set S), and ultimately to measurable manifestations (set M).