Abductive Inference Models for Diagnostic Problem-Solving by Yun Peng

By Yun Peng

Making a prognosis while whatever is going incorrect with a traditional or m- made procedure might be tricky. in lots of fields, comparable to drugs or electr- ics, an extended 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. by contrast, the beginner is just not taught how you can cause with this data in arriving at a end or a analysis, other than might be implicitly via ease examples. this is able to appear to point out that some of the crucial features of diagnostic reasoning are a kind of intuiti- dependent, good judgment reasoning. extra accurately, diagnostic reasoning may be categorized as one of those inf- ence often called abductive reasoning or abduction. Abduction is outlined to be a technique of producing a believable reason for a given set of obs- vations or evidence. even though pointed out in Aristotle's paintings, the research of f- mal features of abduction didn't fairly commence till a few century ago.

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Month of Year: (1) April (2) May (3) June (4) July (5) August (6) September _- ? 3. possibilities now being categorically rejected" Carbon Isotope (The question-answering sequence starts. , a question about a setting factor. Since it occurred in June and the knowledge base indicates that Carbon Isotope is never used in June, this disorder is removed from consideration in this problem. ) pH: (1) Acidic (2) Normal (3) Alkaline _ ? 1. Hypotheses: Generator: Competing possibilities" Benzenesulfonic Acid Carbonic Acid Hydrochloric Acid Sulfuric Acid (The first manifestation, p H Acidic, evokes the four possible chemical contaminants which can make the water acidic.

This termination condition is a somewhat arbitrary approach to deciding when sufficient information has been obtained. While it asks about all attributes relevant to ranking the competing explanations involved at termination time, it might leave some information unsought. For example, there are situations where some present manifestations are not included in the initial complaints, nor are they manifestations of any of the disorders in the current hypotbceses. Such manifestations may be ignored by this question generation method.

This makes the knowledge base easier to create but places a greater burden on the inference mechanism. Given one or more initial problem features, the inference mechanism generates a set of potential plausible hypotheses or "causes" which can explain the given problem features. These hypotheses are then tested by (1) the use of various procedures which measure their ability to account for the known features, and (2) the generation of new questions whose answers will help to discriminate or disambiguate among the most likely hypotheses.

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