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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2009-01-28

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Citations of this work

Generics, Prevalence, and Default Inferences.Sangeet Khemlani, Sarah-Jane Leslie & Sam Glucksberg - 2009 - Proceedings of the Cognitive Science Society:443--8.

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References found in this work

Non-monotonic logic I.Drew McDermott & Jon Doyle - 1980 - Artificial Intelligence 13 (1-2):41-72.
Reasoning.Lance J. Rips - 2002 - In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.

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