Abstract. The task of predicate invention in Inductive Logic Programming is to extend the hypothesis language with new predicates if the vocabulary given initially is insufficient for the learning task. However, whether predicate invention really helps to make learning succeed in the extended language depends on the language bias currently employed. In this paper, we investigate for which commonly employed language biases predicate invention is an appropriate shift operation. We prove that for some restricted languages predicate invention does not help when the learning task fails and we characterize the languages for which predicate invention is useful. We investigate the decidability of the blas shift problem for these languages and discuss the capabilities of predicate invention as a bias shift operation.

The task of predicate invention in ILP is to extend the hypothesis language with new predicates in case that the vocabulary given initially is insufficient for the learning task. However, whether predicate invention really helps to make learning succeed in the extended language depends on the bias that is currently employed. In this paper we investigate for which commonly employed language biases predicate invention is an appropriate shift operation. We prove that for some restricted languages predicate invention does not help in case that the learning task fails, and characterize the languages for which predicate invention is useful as bias shift operation.

Inductive Logic Programming (ILP) is a subfidd of machine learning dealing with inductive inference in a first order Horn clanse fxamework. A problem in ILP is how to extend the hypotheses language in the case that the vocabulary given initially is insufficient. One way to adapt the vocabulary is to introduce new predicates. In this paper, we give an overview of different approaches to predicate invention in ILP. We discuss theoretical results concerning the introduction of new predicates, and ILP-systems capable of inventing predicates.

Abstract. In this paper we describe two methods for improving systems that induce disjunctive Horn clause definitions. The first method is the well-known use of argument types during induction. Our novel contribution is an algorithm for extracting type information from the example set mechanically. The second method provides a set of clause heads partitioning the example set in disjuncts according to structural properties. Those heads can be used in top-down inductive inference systems as starting point of the general-to-specific search and reduce the resulting space of clause bodies.

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