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A non-monotonic [logic] that allows beliefs and conclusions to be revised when new information is received. It formalizes the process of updating a knowledge base: when a new fact contradicts an old [one], the system must retract some beliefs to maintain consistency. Revision [logic] is the logical foundation of belief revision theory, which models how rational agents change their minds. It contrasts with classical monotonic [logic], where adding new premises never invalidates old conclusions. Revision [logic] is essential for AI, machine learning, epistemology, and everyday reasoning – because we learn new things and must sometimes discard old certainties. A common mistake is to think revision [logic] means “anything goes”; actually, it has rigorous rules for which beliefs are retracted and which are preserved. In online debates, revision [logic] is invoked to defend changing one’s position: “I revised my belief because new evidence came in.”
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