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This work introduces defeasible action rules that capture the typical outcome of action, without committing to what happens in exceptional circumstances, and defines an inference operator for defeasible action rules inspired by rational closure, adapted to the dynamic setting.
This work identifies a single structural condition on learned conjunctive rule sets, called strict global exception closure, under which every prediction the classifier makes is provably a defeasible entailment and analyses what breaks when the condition fails, revealing insights for both machine learning and nonmonoto...
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