By Yun Peng
Making a prognosis whilst whatever is going improper with a usual or m- made procedure might be tough. in lots of fields, resembling 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 concerning the category of structures to be clinically determined. against this, the beginner shouldn't be taught how one can cause with this information in arriving at a end or a analysis, other than possibly implicitly via ease examples. this is able to appear to point out that some of the crucial facets of diagnostic reasoning are a kind of intuiti- established, logic reasoning. extra accurately, diagnostic reasoning should be labeled 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 though pointed out in Aristotle's paintings, the research of f- mal facets of abduction didn't particularly begin until eventually a couple of century ago.
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Extra resources for Abductive Inference Models for Diagnostic Problem-Solving
Example text
The first hypothesis contains only one disorder, so it is the smallest cover of the three manifestations and thus is called a m i n i m u m cover. The second hypothesis contains three disorders. This does not look parsimonious or simple, especially compared to the first single-disorder hypothesis. However, this second hypothesis is still parsimonious in the sense that removal of any disorder from it will make it no longer a cover of all present manifestations. Such hypotheses are called irredundant covers because they contain no redundant or extraneous disorders.
This damages the generality of the rule-based system technique as a general model for knowledge-based system construction, especially for constructing knowledge-based diagnostic systems. 2. Association-Based Abductive Models Associative (or semantic) networks have long been studied as a knowledge representation method in AI [Quillian68, Findler79]. An associative network usually consists of nodes, representing entities such as objects, concepts, and events, and links between the nodes, representing the interrelations or associations between nodes.
3. An Example: Chemical Spill! As a first example of a computerized diagnostic system based on parsimonious covering theory, we consider the Chemical Spill System. This system is for a "toy problem" involving diagnosing the cause(s) of a chemical spill contaminating a creek. HT, a domainindependent software program for constructing knowledge-based systems based on parsimonious covering theory. HT is, to the authors' knowledge, the first domain-independent software environment for creating and studying abductive diagnostic problem-solving systems [Reggia82a].