In the last decade, many efforts have been devoted to the exploration of techniques for learning and refining first order theories, as the necessary step for applying machine learning methodologies to real world applications. In this paper, we present a new approach to the integration of inductive and deductive learning techniques that seems to overcome some of the limitations of existing learn...

We study annealed theories of learning boolean functions using a concept class of nite cardinality The naive annealed theory can be used to derive a univer sal learning curve bound for zero temperature learning similar to the inverse square root bound from the Vapnik Chervonenkis theory Tighter nonuniversal learning curve bounds are also derived A more re ned annealed theory leads to still tigh...

In this paper we describe a method of automatically learning domain theories from parsed corpora of sentences from the relevant domain and use FSA techniques for the graphical representation of such a theory. By a ‘domain theory’ we mean a collection of facts and generalisations or rules which capture what commonly happens (or does not happen) in some domain of interest. As language users, we i...

Outcome based or competency based education (OBE) is so firmly established in undergraduate medical education that it might not seem necessary to ask why it was included in recommendations for the future, like the Flexner centenary report. Uncritical acceptance may not, however, deliver its greatest benefits. Our aim was to explore the underpinnings of OBE: its historical origins, theoretical b...

Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model this kind of domains is crucial in “learning to design” tasks, that is, learning applications where the goal is to learn from examples how to perform a...

We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain eeectively learn concepts from a small number of examples, in spite of the fact that each example contains a huge amount of information? We provide a novel analysis for a model of robust concept learning (closely related to \margin classiiers"), and show that a relatively small number of examples ar...

Serious games have been increasingly interesting to educators recently, but it is difficult to find good games that truly address the needs of young learners, especially in mathematics. Some games are either too simplistic or are not actual games. In the paper, an argument is made for the benefits of game-based learning (GBL) for instruction and assessment given what we know about Millenials an...

This paper discusses how to learn theories that are modal, concentrating on the issue of how modal hypotheses are formed. Illustrations are given to show the usefulness of the ideas for agent applications.

In this paper we study the problem of learning abductive theories with particular interest in learning theories for the problem of attribute-based classification as studied in the area of machine learning. The paper proposes a new alternative formulation of this class of learning problems where abduction takes an integral part in the formulation of the appropriate theories and more importantly ...

By a ‘domain theory’ we mean a collection of facts and generalisations or rules which capture what commonly happens (or does not happen) in some domain of interest. As language users, we implicitly draw on such theories in various disambiguation tasks, such as anaphora resolution and prepositional phrase attachment, and formal encodings of domain theories can be used for this purpose in natural...