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Abductive Reasoning and Learning

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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.

Author(s): Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay, Rudolf Kruse (eds.)
Series: Handbook of Defeasible Reasoning and Uncertainty Management Systems 4
Edition: 1
Publisher: Springer Netherlands
Year: 2000

Language: English
Pages: 442
Tags: Logic; Artificial Intelligence (incl. Robotics); Probability Theory and Stochastic Processes; Mathematical Logic and Foundations

Front Matter....Pages i-v
On the Relation between Abduction and Inductive Learning....Pages 1-33
AI Approaches to Abduction....Pages 35-98
Abduction in Labelled Deductive Systems....Pages 99-154
Logical Characterisations of Inductive Learning....Pages 155-196
Abduction in Machine Learning....Pages 197-229
An Overview of Ordinal and Numerical Approaches to Causal Diagnostic Problem Solving....Pages 231-280
Abductive Inference with Probabilistic Networks....Pages 281-314
Learning from Data: Possibilistic Graphical Models....Pages 315-389
Independence in Uncertainty Theories and Its Applications to Learning Belief Networks....Pages 391-434
Back Matter....Pages 435-442