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...

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...

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.