The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed.â€¦ (More)

We present the K.U.Leuven CHR system: what started out as a validation of a new attributed variables implementation, has become a part of three different Prolog systems with an increasing userbase.â€¦ (More)

When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques areâ€¦ (More)

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the eÃ†ciency of ILP systemsâ€¦ (More)

This paper illustrates the application of abstract compilation using multiple incarnations of the domain Prop in deriving type dependencies for logic programs. We illustrate how dependencies can beâ€¦ (More)

Constraint Handling Rules (CHR) is a high-level rule-based programming language which is increasingly used for general-purpose programming. We introduce the CHR machine, a model of computation basedâ€¦ (More)

The past few years have seen a surge of interest in the field of probabilistic logic learning or statistical relational learning. In this endeavor, many probabilistic logics have been developed.â€¦ (More)

The copying approach to tabling (CAT) is an alternative to SLG-WAM and based on incrementally copying the areas that the SLG-WAM freezes to preserve execution states of suspended computations. Theâ€¦ (More)

This paper illustrates the role of a class of \prop"-ositional logic programs in the analysis of complex properties of logic programs. Analyses are performed by abstracting Prolog programs toâ€¦ (More)