Abductive Reasoning and Learning by Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay,

By Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay, Rudolf Kruse (eds.)

This publication includes top survey papers at the a number of elements of Abduction, either logical and numerical techniques. Abduction is valuable to all parts of utilized reasoning, together with man made intelligence, philosophy of technological know-how, computing device studying, info mining and selection thought, in addition to good judgment itself.

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Here we initially have some abductive hypotheses regarding the presence or absence of abducible assumptions. Based on these hypotheses and other data in T we generate, by means of confirmatory induction, new sentences I which, when interpreted as integrity constraints on the new theory T, would support the abducible assumptions (assumptions of presence would be consistent with I, assumptions of absence would now be inconsistent with I). This type of cooperation between abductive and inductive reasoning is based 28 PETER A.

Muggleton and Buntine, 1988) Stephen H. Muggleton and Wray L. Buntine. Machine invention of first-order predicates by inverting resolution. In Proceedings of the Fijih International Conference on Machine Learning, pages 339-352, San Mateo, CA, 1988. Morgan Kaufmann. [Muggleton and De Raedt, 1994] Stephen H. Muggleton and Luc De Raedt. Inductive logic programming: theory and methods. Journal of Logic Programming, 19-20:629-679, 1994. [Muggleton, 1995) Stephen H. Muggleton. Inverse entailment and Progol.

Thereby they use a qualitative notion of plausibility. As they argue, the definition of an abductive explanation in terms of deductive inference results in several shortcomings: • The defeasibility of abductive explanations cannot be expressed by monotonic derivability.

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