Abductive Inference Models for Diagnostic Problem-solving (Symbolic computation - artificial intelligence) - Hardcover

Peng, Yun; Reggia, James A.

Hardcover

ISBN 10: 3540973435 ISBN 13: 9783540973430

Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 1990

Synopsis

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.

Edition details

• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
• Publication date: 1990
• Language: English
• ISBN 10: 3540973435
• ISBN 13: 9783540973430
• Binding: Hardcover
• Number of pages: 296