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Abstract. The available concept-learners only partially fulfill the needs imposed by the learning apprentice generation of learners. We present a novel approach to interactive concept-learning and constructive induction that better fits the requirements imposed by the learning apprentice paradigm. The approach is incorporated in the system Clint-Cia, which integrates several user-friendly features into one working whole: it is interactive, generates examples, shifts its bias, identifies concepts in the limit, copes with indirect relevance, recovers from errors, performs constructive induction and invents new concepts by analogy to previously learned ones.

Editor: M. des Jardins and D. Gordon Abstract. A comparative study is presented of language biases employed in specific-to-general learning systems within the Inductive Logic Programming (ILP) paradigm. More specifically, we focus on the biases employed in three well known systems: CLINT, GOLEM and ITOU, and evaluate both conceptually and empirically their strengths and weaknesses. The evaluation is carried out within the generic framework of the NINA system, in which bias is a parameter. Two different types of biases are considered: syntactic bias, which defines the set of well-formed clauses, and semantic bias, which imposes restrictions on the behaviour of hypotheses or clauses. NINA is also able to shift its bias (within a predefined series of biases), whenever its current bias is insufficient for finding complete and consistent concept definitions. Furthermore, a new formalism for specifying the syntactic bias of inductive logic programming systems is introduced.

Clustering is used in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries to the system. In cluster-oriented systems, the retrieval process can be enhanced by employing characterization of clusters. In this paper, we present the techniques to develop clusters and cluster characterizations by employing user viewpoint. The user viewpoint is elicited through a structured interview based on a knowledge acquisition technique, namely personal construct theory. It is demonstrated that the application of personal construct theory results in a cluster representation that can be used during query as well as to assign new documents to the approp...

Interest in adaptable Machine Learning systems grows as the number of concept learning applications increases. We present here a generic algorithm expressed in terms of elementary learning operations and biases that control these operations. By shifting the selection of learning operations and biases, one gets different Machine Learning systems in terms of representation language, complexity and learning results. This report develops the work of [ Mitchell, 1982 ] and [ De Raedt and Bruynooghe, 1992 ] by eliciting learning strategies in Generate-and-Test systems. The generic Generateand -Test algorithm we present has been implemented in a system called HAIKU, as a framework to study and compare the effects of the choice of biases and learning operators on the characteristics of the learning process and the learning results. Keywords: Symbolic Machine Learning, Inductive Logic Programming, Declarative Bias, Parameterisation of ML systems. 1 Introduction 1.1 Motivations Eliciting the ...