Getting adaptability or expressivity in inductive logic programming by using fuzzy predicates

Introducing fuzzy predicates in inductive logic programming may serve two different purposes: getting more expressivity by learning fuzzy rules or allowing for more adaptability when learning classical rules. On the one hand, we can thus learn gradual and certainty rules, which have an increased expressive power and have no simple crisp counterpart. On the other hand, fuzzy predicates in rules can be used for discretization when the database contains numerical attributes. In this case the fuzzy counterparts of crisp rules allow us to check the meaningfulness and the accuracy of the crisp rules. We formally describe the computation of the confidence degrees for each type of rules with fuzzy predicates. Next, we discuss the interest and the application domain of each kind of rules with fuzzy predicates.