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Interest in Declarative bias in Machine Learning is growing with the expressivity of the concept description language of ML systems. Inductive Logic Programming more than any other ML eld is thus concerned with explicitely biasing learning. The main issues already identiied in declarative bias RG90] have been studied within the ILP project, i.e. the(More)
As each of the four main approaches to a declarative bias represention in Inductive Logic Programming (ILP), the representation by parameterized languages or by clause sets, the grammar-based and the scheme-based representation, fails in representing all language biases in ILP systems, we present a unifying representation language MILES-CTL for these biases(More)
Restrictions on the number and depth of existential variables as deened in the language series of Clint Rae92] are widely used in ILP and expected to produce a considerable reduction in the size of the hypothesis space. In this paper we show that this is generally not the case. The lower bounds we present lead to intractable hypothesis spaces except for toy(More)