Human benchmarks on ai's benchmark problems

Abstract
Default reasoning occurs when the available information does not deductively guarantee the truth of the conclusion; and the conclusion is nonetheless correctly arrived at. The formalisms that have been developed in Artificial Intelligence to capture this mode of reasoning have suffered from a lack of agreement as to which non-monotonic inferences should be considered correct; and so Lifschitz 1989 produced a set of “Nonmonotonic Benchmark Problems” which all future formalisms are supposed to honor. The present work investigates the extent to which humans follow the prescriptions set out in these Benchmark Problems.
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Beihai Zhou & Yi Mao (2006). A Base Logic for Default Reasoning. Frontiers of Philosophy in China 1 (4):688-709.
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