Non-monotonic inference

Abstract Non-monotonic inference is inference that is defeasible: in contrast with deductive inference, the conclusions drawn may be withdrawn in the light of further information, even though all the original premises are retained. Much of our everyday reasoning is like this, and a non-monotonic approach has applications to a number of technical problems in artificial intelligence. Work on formalizing non-monotonic inference has progressed rapidly since its beginnings in the 1970s, and a number of mature theories now exist – the most important being default logic, autoepistemic logic, and circumscription.
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    G. Aldo Antonelli (1996). Defeasible Reasoning as a Cognitive Model. In Krister Segerberg (ed.), The Parikh Project. Seven Papers in Honour of Rohit. Uppsala Prints & Preprints in Philosophy.
    James Hawthorne (1988). A Semantic Approach to Non-Monotonic Conditionals. In J. F. Lemmer & L. N. Kanal (eds.), Uncertainty in Artificial Intelligence 2. Elsevier.
    G. Aldo Antonelli, Non-Monotonic Logic. Stanford Encyclopedia of Philosophy.

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