Abstract
Nonmonotonic inference is inference that is defeasible: in contrast with deductive inference, the conclusions drawn may be withdrawn in the light of further information, even though all the original premises are retained.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 nonmonotonic approach has applications to a number of technical problems in artificial intelligence. Work on formalizing nonmonotonic 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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