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• W2438298867 abstract "We consider a new formulation of abduction in which degrees of plausibility of explanations, along with the rules of the domain, are learned from concrete examples (settings of attributes). Our version of abduction thus falls in the learning to reason framework of Khardon and Roth. Such approaches enable us to capture a natural notion of plausibility in a domain while avoiding the extremely difficult problem of specifying an explicit representation of what is plausible. We specifically consider the question of which syntactic classes of formulas have efficient algorithms for abduction. We find that the class of k-DNF explanations can be found in polynomial time for any fixed k; but, we also find evidence that even weak versions of our abduction task are intractable for the usual class of conjunctions. This evidence is provided by a connection to the usual, inductive PAC-learning model proposed by Valiant. We also consider an exception-tolerant variant of abduction. We observe that it is possible for polynomial-time algorithms to tolerate a few adversarially chosen exceptions, again for the class of k-DNF explanations. All of the algorithms we study are particularly simple, and indeed are variants of a rule proposed by Mill." @default.
• W2438298867 created "2016-06-24" @default.
• W2438298867 creator A5064954992 @default.
• W2438298867 date "2016-02-21" @default.
• W2438298867 modified "2023-10-16" @default.
• W2438298867 title "Learning Abductive Reasoning Using Random Examples" @default.
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• W2438298867 doi "https://doi.org/10.1609/aaai.v30i1.10099" @default.
• W2438298867 hasPublicationYear "2016" @default.
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