Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
In this paper we present a method for semantic annotation of texts, which is based on a deep linguistic analysis (DLA) and Inductive Logic Programming (ILP). The combination of DLA...
The robot described in this paper learns words that relate to objects and their attributes and also learns concepts, which may be recursive, that involve relationships between sev...
The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently a chal...
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall i...
This paper describes experiments on learning Dutch phonotactic rules using Inductive Logic Programming, a machine learning approach based on the notion of inverting resolution. Di...
This paper presents an overview of recent systems for Inductive Logic Programming (ILP). After a short description of the two popular ILP systems FOIL and Progol, we focus on meth...
This paper describes the use and customization of Inductive Logic Programming (ILP) to infer indexing rules from MEDLINE citations. Preliminary results suggest this method may enh...
This paper presents a method to induce relational concepts with neural networks using the inductive logic programming system LINUS. Some first-order inductive learning tasks taken...
Rodrigo Basilio, Gerson Zaverucha, Artur S. d'Avil...
We introduce the graph-based relational concept learner SubdueCL. We start with a brief description of other graph-based learning systems: the Galois lattice, Conceptual Graphs, a...
Jesus A. Gonzalez, Lawrence B. Holder, Diane J. Co...