Download Abductive Inference Models for Diagnostic Problem-Solving by Yun Peng PDF
By Yun Peng
Making a prognosis while anything is going flawed with a usual or m- made approach will be tricky. in lots of fields, comparable to drugs or electr- ics, a protracted education interval and apprenticeship are required to turn into a talented diagnostician. in this time a beginner diagnostician is requested to assimilate a large number of wisdom in regards to the type of platforms to be clinically determined. against this, the beginner seriously isn't taught tips to cause with this data in arriving at a end or a prognosis, other than maybe implicitly via ease examples. this might appear to point out that the various crucial facets of diagnostic reasoning are one of those intuiti- dependent, good judgment reasoning. extra accurately, diagnostic reasoning may be categorised as one of those inf- ence referred to as abductive reasoning or abduction. Abduction is outlined to be a technique of producing a believable reason behind a given set of obs- vations or proof. even supposing pointed out in Aristotle's paintings, the research of f- mal features of abduction didn't fairly begin until eventually a couple of century ago.
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Additional info for Abductive Inference Models for Diagnostic Problem-Solving
The advantage of minimality is that it is more focused, thus resulting in a smaller number of hypotheses to consider in the final diagnosis. Some Issues 39 Irredundancy, on the other hand, is more inclusive: a minimum cover is also irredundant but not vice versa. Thus, the irredundancy condition usually results in more, sometimes many more, final hypotheses (explanations) than the minimality condition. For example, at the end of the diagnostic session, the Chemical Spill System produced a single generator representing only six minimum covers for the given manifestations.
To determine what characteristics make a set of disorders a plausible or "best" or "simplest" explanation for a given set of manifestations is a very important issue for automated diagnostic systems as well as for other abductive inference applications. This problem has not been resolved satisfactorily. In both the Chemical Spill System and Dizziness Diagnostic System examples above, minimum cardinality was used as the criterion for parsimony. In other words, these systems adopt the notion that smaller hypotheses (containing fewer disorders) are better or more plausible than larger ones, an approach advocated by other frameworks for abduction [Pople82, Thagard89].
It is also easy to check that each hypothesis generated is a cover. For example, consider the hypothesis Radium and Benzenesulfonic Acid whose two disorders are taken from different competing groups of the generator. Radium covers Spectrometry = Metal, Radioactivity = Present, and Specific Gravity = Increased; while Benzenesulfonic Acid covers p H Acidic and Spectrometry = Carbon. Also note that both chemicals are used in the month of June. Thus these six hypotheses can be concluded to be minimum covers.