Toward an Improved Downward Refinement Operator for Inductive Logic Programming

In real-world supervised Machine Learning tasks, the learned theory can be deemed as valid only until there is evidence to the contrary (i.e., new observations that are wrongly classified by the theory). In such a case, incremental approaches allow to revise the existing theory to account for the new evidence, instead of learning a new theory from scratch. In many cases, positive and negative examples are provided in a mixed and unpredictable order, which requires generalization and specialization refinement operators to be available for revising the hypotheses in the existing theory when it is inconsistent with the new examples. The space of Datalog Horn clauses under the OI assumption allows the existence of refinement operators that fulfill desirable properties. However, the versions of these operators currently available in the literature are not able to handle some refinement tasks. The objective of this work is paving the way for an improved version of the specialization operator, aimed at extending its applicability.