Abductive Inference Models for Diagnostic Problem-Solving

Name: Abductive Inference Models for Diagnostic Problem-Solving
Brand: Springer-Verlag New York Inc.
SKU: 9781461264507
Price: 423.49 PLN
Availability: InStock

Autor: Yun Peng, James A. Reggia

Język:

Oprawa: Miękka

Wydawca: Springer-Verlag New York Inc.

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This book is about reasoning with causal associations during diagnostic problem-solving. It formaliz...

Informacje o książce

Autor

Yun Peng, James A. Reggia

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2012

strony

285

EAN

9781461264507

ISBN

1461264502

Enbook ID

02179588

Wydawca

Springer-Verlag New York Inc.

Waga

462

Wymiary

155 x 235 x 17

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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.

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