No abstract

No abstract

No abstract

We describe a methodology for upgrading existing attribute value learners towards rst order logic. This method has several advantages: one can pro t from existing research on propositional learners (and inherit its e ciency and e ectiveness), 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.

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 rst order upgrades of typical data mining systems, which induce association rules, classi cation rules or decision trees respectively.