It is well known that modeling with constraints networks require a fair expertise. Thus tools
able to automatically generate such networks have gained a major interest. The major …

In this paper we present a hybrid graph-based concept discovery method. Concept rule
discovery aims at finding the definition of a specific concept in terms of relations involving …

The feasibility of symbolic learning strongly relies on the efficiency of heuristic search in the
hypothesis space. However, recent works in relational learning claimed that the phase …

Multi-relational concept discovery aims to find the relational rules that best describe the
target concept. In this paper, we present a graph-based concept discovery method in Multi …

Abstract Genetic Algorithms (GAs) are known for their capacity to explore large search
spaces and due to this ability they were applied (to some extent) to Inductive Logic …

We study reducibility of examples in several typical inductive logic programming
benchmarks. The notion of reducibility that we use is related to theta-reduction, commonly …

Developments in technology, especially in computer science created the need of storing
data in variety of areas. This need created the term database where the data is stored in a …

It is known recently that relational learning is Σ2 hard. This level of complexity is due to the
entanglement of two NP problems: the covering test problem and the search problem in …

It is well known that for certain relational learning problems, traditional top-down search falls
into blind search. Recent works in Inductive Logic Programming about phase transition and …

With the increasing amount of information in electronic form the fields of Machine Learning
and Data Mining continue to grow by providing new advances in theory, applications and …