On the relation between abduction and inductive learning

Peter A. Flach and Antonis C. Kakas. In Dov M. Gabbay and Rudolf Kruse, editors, Handbook of defeasible reasoning and uncertainty management systems, Vol. 4: Abductive reasoning and learning, pages 1--33. Kluwer Academic Publishers, October 2000.

Abstract

This Handbook volume is devoted to abduction and learning as they appear in various subfields of artificial intelligence. Broadly speaking, abduction aims at finding explanations for, or causes of, observed phenomena or facts. Learning occurs when an agent adapts its state or behaviour on the basis of experience. Both processes have a number of common characteristics. They both aim at improving their picture or model of the universe of discourse; they are both hypothetical, in the sense that the results may be wrong; and they can both be seen as reasoning processes, with the observations and the current knowledge of the world as input statements and the learned or abduced hypotheses as output statements. In the case of learning from examples, which is the most common form of learning studied in artificial intelligence, this form of reasoning is called induction, and that is the term we will be mostly using in this chapter.

Given these common characteristics of abduction and induction, it makes sense to study them together. Once we have a clear picture of their similarities as well as their differences, understanding one contributes to the understanding of the other. Such an integrated study is the subject of this introductory chapter. As abduction and induction have been studied in philosophy and logic as well as artificial intelligence, we review selected approaches from each of these disciplines before attempting to come up with an integrated perspective.

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On the relation between abduction and inductive learning

Abstract

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