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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