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 these advantages by declaring and shiftin...

Humans improve their performance by means of a variety of learning strategies, including both gradual statistical induction from experience and rapid incorporation of advice. In many learning environments, these strategies may interact in complementary ways. The focus of this work is on cognitively plausible models of multistrategy learning involving the integration of inductive generalization ...

This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer s...

In this paper we review the applicability of representative inductive machine learning approaches in multicriteria decision making. We limit our review to four systems. We use SICLA and KBG as representative conceptual clustering systems and ID3 and CN2 as representative learning from examples systems. We demonstrate our results by way of two real world decision making exemplars. The first exem...

We study the integration of background knowledge and concept learning genetic algorithms and show how they have been integrated in the system DOGMA Our emphasis is in speeding up the inductive learning process by using suggestions from the background knowledge to direct genetic search We don t do theory revision by patching the old theory rather we build a new theory by using parts of the backg...

This paper explores how inductive machine learning can guide the breeding process of evolutionary algorithms for black-box function optimization. In particular, decision trees are used to identify the underlying characteristics of good and bad individuals, using the mined knowledge for wise breeding purposes. Inductive learning is complemented with statistical learning in order to define the br...

This paper briefly surveys the state of the art of a particular mechanism of learning: induction. We discuss inductive mechanisms, drawing attention to the foundation of generalisation success and its limitations. The distinction between base-learning and meta-learning approaches is pointed out in order to better identify the peculiar attributes of current learning strategies. We examine the po...

We compared the effects of spaced versus massed practice on young and older adults' ability to learn visually complex paintings. We expected a spacing advantage when 1 painting per artist was studied repeatedly and tested (repetition) but perhaps a massing advantage, especially for older adults, when multiple different paintings by each artist were studied and tested (induction). We were surpri...

Many real-world decision-making problems fall into the general category of classification. Algorithms for constructing knowledge by inductive inference from example have been widely used for some decades. Although these learning algorithms frequently address the same problem of learning from preclassified examples and much previous work in inductive learning has focused on the algorithms’ predi...

Inductive Logic Programming (ILP) is a study of machine learning systems that use clausal theories in first-order logic as a representation language. In this paper, we survey theoretical foundations of ILP from the viewpoints of Logic of Discovery and Machine Learning, and try to unify these two views with the support of the modern theory of Logic Programming. Firstly, we define several hypothe...