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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)
In this paper, we aim to give a more precise deenition of bias in order to clarify diierent views. Since this deenition involves the problem setting of inductive learning, we have to take into account this deenition as well. A more precise deenition of the bias can be used to declare, to adapt and to shift the bias of an inductive system. We demonstrate(More)