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Learning recursive theories with the separate-and-parallel-conquer strategy

Inductive learning of recursive logical theories from a set of examples is a complex task characterized by three important issues that do not arise in single predicate learning, namely the adoption of a generality order stronger than θ-subsumption, the non-monotonicity of the consistency property, and the automated discovery of dependencies between target predicates. Some variations of the classical separate-and-conquer strategy to overcome these issues have been implemented in the learning system ATRE and are reported in this paper. An application to the document image understanding domain is discussed and conclusions are drawn.