Introduces a step toward the formalization of abductive inference: a mathematical model of diagnostic reasoning the authors call parsimonious covering theory. Intended for readers with a background in artificial intelligence or cognitive science, and assumes a basic knowledge of elementary set theory, logic, and probability theory. Annotation copyright Book News, Inc. Portland, Or.
Making a diagnosis when something goes wrong with a natural or m- made system can be difficult. In many fields, such as medicine or electr- ics, a long training period and apprenticeship are required to become a skilled diagnostician. During this time a novice diagnostician is asked to assimilate a large amount of knowledge about the class of systems to be diagnosed. In contrast, the novice is not really taught how to reason with this knowledge in arriving at a conclusion or a diagnosis, except perhaps implicitly through ease examples. This would seem to indicate that many of the essential aspects of diagnostic reasoning are a type of intuiti- based, common sense reasoning. More precisely, diagnostic reasoning can be classified as a type of inf- ence known as abductive reasoning or abduction. Abduction is defined to be a process of generating a plausible explanation for a given set of obs- vations or facts. Although mentioned in Aristotle's work, the study of f- mal aspects of abduction did not really start until about a century ago.
This book is about reasoning with causal associations during diagnostic problem-solving. The authors develop a mathematical model called parsimonious covering theory and link it with probability theory.
