On the Relation between Abduction and Inductive Learning; P.A. Flach, A.C. Kakas. Part I: Logical Approaches. AI Approaches to Abduction; G. Paul. Abduction in Labelled Deductive Systems; D.M. Gabbay. Logical Characterisations of Inductive Learning; P.A. Flach. Abduction in Machine Learning; F. Bergadano, V. Cutello, D. Gunetti. Part II: Numerical Approaches. An Overview of Ordinal and Numerical Approaches to Causal Diagnostic Problem Solving; D. Dubois, H. Prade. Abductive Inference with Probabilistic Networks; C. Borgelt, R. Kruse. Learning from Data: Possibilistic Graphical Models; J. Gebhardt. Independence in Uncertainty Theories and its Applications to Learning Belief Networks; L.M. de Campos, J.F. Huete, S. Moral. Index.

This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches. Abduction is central to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision theory, as well as logic itself.