Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive
and negative examples with respect to background knowledge expressed as logic …

Event recognition systems rely on knowledge bases of event definitions to infer occurrences
of events in time. Using a logical framework for representing and reasoning about events …

Inductive logic programming (ILP) realizes inductive machine learning in computational
logic. However, the present ILP mostly handles classical clausal programs, especially Horn …

Induction of recursive theories in the normal ILP setting is a difficult learning task whose
complexity is equivalent to multiple predicate learning. In this paper we propose …

The ability to generalise from a small number of examples is a fundamental challenge in
machine learning. To tackle this challenge, we introduce an inductive logic programming …

Nonmonotonic logic programming (NMLP) and inductive logic programming (ILP) are two
important extensions of logic programming. The former aims at representing incomplete …

Inverse entailment (IE) is known as a technique for finding inductive hypotheses in Horn
theories. When a background theory is nonmonotonic, however, IE is not applicable in its …

Event recognition systems rely on knowledge bases of event definitions to infer occurrences
of events in time. Using a logical framework for representing and reasoning about events …

This work presents the application of theory revision to the design of distributed databases to
automatically revise a heuristic-based algorithm (called analysis algorithm) through the use …

We provide a precise illustration of what can be the idea of “computational creativity”, that is,
the whole set of the methods by which a computer may simulate creativity. This paper is …