A logical framework for default reasoning
Artificial Intelligence
Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Abductive consequence relations
Artificial Intelligence
Jumping to explanations versus jumping to conclusions
Artificial Intelligence
Rationality Postulates for Induction
Proceedings of the Sixth Conference on Theoretical Aspects of Rationality and Knowledge
Abduction in Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Relations between the logic of theory change and nonmonotonic logic
Proceedings of the Workshop on The Logic of Theory Change
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Abduction was first introduced in the epistemological context of scientific discovery. It was more recently analyzed in artificial intelligence, especially with respect to diagnosis analysis or ordinary reasoning. These two fields share a common view of abduction as a general process of hypotheses formation. More precisely, abduction is conceived as a kind of reverse explanation where a hypothesis H can be abduced from events E if H is a "good explanation" of E. The paper surveys four known schemes for abduction that can be used in both fields. Its first contribution is a taxonomy of these schemes according to a common semantic framework based on belief revision. Its second contribution is to produce, for each non-trivial scheme, a representation theorem linking its semantic framework to a set of postulates. Its third contribution is to present semantic and axiomatic arguments in favor of one of these schemes, "ordered abduction," which has never been vindicated in the literature.