Inductive Logic Programming (ILP) deals with inducing clausal theories from examples basically through generalization or specialization. The specialization and generalization oper...
Abstract. Knowledge discovery is a time-consuming and space intensive endeavor. By distributing such an endeavor, we can diminish both time and space. System INDEDpronounced indee...
Jennifer Seitzer, James P. Buckley, Yi Pan, Lee A....
In the line of previous work by S. Muggleton and C. Sakama, we extend the logical characterization of inductive logic programming, to normal logic programs under the stable models ...
Over the last few years, a few approaches have been proposed aiming to combine genetic and evolutionary computation (GECCO) with inductive logic programming (ILP). The underlying r...
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. Our research has focused on Information Extraction (IE), a task that typically invol...
Learning systems have been devised as a way of overcoming the knowledge acquisition bottleneck in the development of knowledge-based systems. They often cast learning to a search p...
Nicola Di Mauro, Floriana Esposito, Stefano Ferill...
Learning general truths from the observation of simple domains and, further, learning how to use this knowledge are essential capabilities for any intelligent agent to understand ...
Paulo Santos, Derek R. Magee, Anthony G. Cohn, Dav...
We propose a new approach to Inductive Logic Programming that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computati...
Abstract. It is well known by Inductive Logic Programming (ILP) practioners that ILP systems usually take a long time to find valuable models (theories). The problem is specially ...
Nuno A. Fonseca, Fernando M. A. Silva, Rui Camacho
Abstract. Learning from multi-relational domains has gained increasing attention over the past few years. Inductive logic programming (ILP) systems, which often rely on hill-climbi...