Empowered negative specialization in inductive logic programming

In symbolic Machine Learning, the incremental setting allows to refine/revise the available model when new evidence proves it is inadequate, instead of learning a new model from scratch. In particular, specialization operators allow to revise the model when it covers a negative example. While specialization can be obtained by introducing negated preconditions in concept definitions, the state-of-the-art in Inductive Logic Programming provides only for specialization operators that can negate single literals. This simplification makes the operator unable to find a solution in some interesting real-world cases. This paper proposes an empowered specialization operator for Datalog Horn clauses. It allows to negate conjunctions of pre-conditions using a representational trick based on predicate invention. The proposed implementation of the operator is used to study its behavior on toy problems purposely developed to stress it. Experimental results obtained embedding this operator in an existing learning system prove that the proposed approach is correct and viable even under quite complex conditions