The state-of-the-art in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, of 2) theory, techniques and applications, of 3) various technical problems addressed within inductive logic programming.

This paper explores the proposition that inductive learning from examples is fundamentally limited to learning only a small fraction of the total space of possible hypotheses. We begin by deening the notion of an algorithm reliably learning a good approximation to a concept C. An empirical study of three algorithms (the classical algorithm for maximally speciic conjunctive generalizations, ID3,...

This thesis introduces a new Machine Learning technique called Hybrid Abductive Inductive Learning (HAIL) that integrates Abductive Logic Programming (ALP) and Inductive Logic Programming (ILP) in order to automate the learning of first-order theories from examples and prior knowledge. A semantics is proposed called Kernel Set Subsumption (KSS) that generalises the well-known inference method o...

objectives: this study aimed to understand the learning strategies of parents of preterm infants, hospitalized in nicu. methods: this study was performed by qualitative research approach. twelve participants including nine parents whose infants were admitted in nicu, two nurses and one physician, all selected by purposive sampling method were interviewed by a female expert occupational therapis...

This paper presents an idea of inductive learning use for rule generation from ontologies. The main purpose of the paper is to evaluate the possibility of inductive learning use in rule generation from ontologies and to develop the way how this can be done. Generated rules are necessary to supplement or even to develop the Semantic Web Expert System (SWES) knowledge base. The SWES emerges as th...

Inductive learning is the learning that is based on induction. In inductive learningDecision tree algorithms are very famous. For the appropriate classification of the objects with the given attributes inductive methods use these algorithms basically. These algorithms are very important in the classification of the objects. That is why many of these algorithms are used in the intelligent system...

In this paper we combine the theory of probability aggregation with results of machine learning theory concerning the optimality of predictions under expert advice. In probability aggregation theory several characterisation results for linear aggregation exist. However, in linear aggregation weights are not fixed, but free parameters. We show how fixing such weights by successbased scores allow...

This paper presents an approach that integrates notions and techniques from two distinct fields of study —namely inductive learning and argumentation in multiagent systems (MAS). We will first discuss inductive learning and the role argumentation plays in multiagent inductive learning. Then we focus on how inductive learning can be used to realize argumentation in MAS based on empirical grounds...

A hallmark of visual intelligence is the ability to extract relationships among objects. One form of extraction produces stimulus-specific knowledge (statistical learning). Another form produces stimulus-general principles (inductive learning). These two learning processes seem incompatible on the surface, but may be related on a deeper level. Here we examine how statistical learning and induct...