Talks about reasoning with causal associations during diagnostic problem-solving. This book develops a mathematical model called parsimonious covering theory and links it with probability theory.

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.

Contents: Abduction and Diagnostic Inference * Computational Models for Diagnostic Problem Solving * Basics of Parsimonious Covering Theory * Probabilistic Causal Model * Diagnostic Strategies in the Probabilistic Causal Model * Causal Chaining * Parallel Processing for Diagnostic Problem-Solving * Conclusion * Bibliography * Index.