By Yun Peng
Making a analysis whilst anything is going improper with a normal or m- made approach could be tricky. in lots of fields, comparable to drugs or electr- ics, a protracted education interval and apprenticeship are required to develop into a talented diagnostician. in this time a beginner diagnostician is requested to assimilate a large number of wisdom in regards to the classification of structures to be clinically determined. against this, the beginner seriously isn't taught easy methods to cause with this information in arriving at a end or a prognosis, other than probably implicitly via ease examples. this might appear to point out that a few of the crucial features of diagnostic reasoning are a kind of intuiti- established, logic reasoning. extra accurately, diagnostic reasoning will be labeled as one of those inf- ence often called abductive reasoning or abduction. Abduction is outlined to be a technique of producing a believable cause of a given set of obs- vations or evidence. even if pointed out in Aristotle's paintings, the examine of f- mal elements of abduction didn't quite commence till a couple of century ago.
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Additional resources for Abductive Inference Models for Diagnostic Problem-Solving
It turns out that these are the only minimum covers for the given manifestations in this example. As noted earlier, even in this small system, there are total of 214 = 16,384 different sets of disorders. Among them, 91 sets have two disorders. The problem-solving system demonstrated above manages to identify all six plausible hypotheses (taking only minimum covers to be "plausible") Association-Based Abductive Models 33 from this large search space and thus shows the computational effectiveness of applying parsimonious covering theory.
Some representative models of both domainspecific and domain-independent systems of association-based abduction and their limitations will be further discussed in Chapter 3 to compare them with parsimonious covering theory. 1. Three models for constructing automated diagnostic systems. 1). 2. Parsimonious Covering Theory: An Informal Preview Parsimonious covering theory is aimed at providing a theoretical foundation for association-based abductive models by formalizing the abductive nature of the diagnostic reasoning process.
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. This is in contrast to statistical or rule-based models where relationships between individual entities are often implicitly embedded in conditional probabilities or conditional rules and often interwoven.
Abductive Inference Models for Diagnostic Problem-Solving by Yun Peng