Abductive Inference Models for Diagnostic Problem-Solving (Hardcover, 1990 ed.)

This book is about reasoning with causal associations during diagnostic problem-solving. It formalizes several currently vague notions of abductive inference in the context of diagnosis. The result is a mathematical model of diagnostic reasoning called parsimonious covering theory. Within this diagnostic, problems and important relevant concepts are formally defined, properties of diagnostic problem-solving are identified and analyzed, and algorithms for finding plausible explanations in different situations are given along with proofs of their correctness. Another feature of this book is the integration of parsimonious covering theory and probability theory. Based on underlying cause-effect relations, the resulting probabilistic causal model generalized Bayesian classification to diagnostic problems where multiple disorders (faults) may occur simultaneously. Both sequential best-first search algorithms and parallel connectionist (neural network) algorithms for finding the most probable hypothesis are provided. This book should appeal to both theoretical researchers and practitioners. For researchers in artificial intelligence and cognitive science, it provides a coherent presentation of a new theory of diagnostic inference. For engineers and developers of automated diagnostic systems or systems solving other abductive tasks, the book may provide useful insights, guidance, or even directly workable algorithms.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Symbolic Computation
Release date:	June 1990
First published:	1990
Authors:	Yun Peng • James A. Reggia
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Hardcover
Pages:	285
Edition:	1990 ed.
ISBN-13:	978-0-387-97343-2
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	0-387-97343-5
Barcode:	9780387973432

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