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Inductive Inference of Prolog Programs with Linear Data Dependency from Positive Data

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概要 In this paper, we study inductive inference of a subclass of Prolog programs from positive examples. The subclass, called linearly covering programs, allows shared variables occurring only in the bodi...es of a clause, which are excluded in the class of linear Prologs already known to be inferable from positive data. In a linearly covering programs, any data passing between subgoals preserves the total size of data contents from a data-dependency constraint. Using Shinohara's work on the inferability of concept defining framework, we prove that for every fixed $ k,m > 0 $, if the length of subgoals in a clause is bounded by a k, the class of linearly covering programs containing at most m definite clauses is inferable from positive data, without any oracle for auxiliary predicates. The result gives a partial answer to the theoretical term problem in model inference. Furthermore, we show that the restriction on the length of bodies is necessary for the inferability.続きを見る

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登録日 2009.04.22
更新日 2017.01.20