Wim Van Laer

Inductive Constraint Logic

A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the algorithm. The resulting theory is incorporated in a system named ICL (Inductive Constraint Logic).

Three companions for data mining in first order logic

Three companion systems, CLAUDIEN, ICL and TILDE, are presented. They use a common representation for examples and hypotheses: each example is represented by a relational database. This contrasts with the classical inductive logic programming systems such as PROGOL and FOIL. It is argued that this representation is closer to attribute value learning and hence more natural. Furthermore, the three systems can be considered first order upgrades of typical data mining systems, which induce association rules, classification rules or decision trees respectively.

How to upgrade propositional learners to first order logic: a case study

We describe a methodology for upgrading existing attribute-value learners towards first-order logic. This method has several advantages: one can profit from existing research on propositional learners (and inherit its efficiency and effectiveness), relational learners (and inherit its expressiveness) and PAC-learning (and inherit its theoretical basis). Moreover there is a clear relationship between the new relational system and its propositional counterpart. This makes the ILP system easy to use and understand by users familiar with the propositional counterpart. We demonstrate the methodology on the ICL system which is an upgrade of the propositional learner CN2.

On Multi-class Problems and Discretization in Inductive Logic Programming

In practical applications of machine learning and knowledge discovery, handling multi-class problems and real numbers are important issues. While attribute-value learners address these problems as a rule, very few ILP systems do so. The few ILP systems that handle real numbers mostly do so by trying out all real values applicable, thus running into efficiency or overfitting problems.

Detecting Traffic Problems with ILP

Expert systems for decision support have recently been suc- cessfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occured (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational back- ground knowledge on the road network is essential. In this paper, we apply three state-of-the art ILP systems to learn how to detect traffic problems and compare their performance to the performance of a propositional learning system on the same problem.

Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms

Inductive Logic Programming considers almost exclusively universally quantified theories. To add expressiveness we should consider general prenex conjunctive normal forms (PCNF) with existential variables. ILP mostly uses learning with refinement operators. To extend refinement operators to PCNF, we should first extend substitutions to PCNF. If one substitutes an existential variable in a formula, one often obtains a specializtion rather than a generalization. In this article we define substitutions to specialize a given PCNF and a weakly complete downward refinement operator. Based on this operator, we have implemented a simple learning system PCL on some type of PCNF.