This thesis is rooted in the field of Inductive Logic Programming (ILP), and, in particular, Meta-Interpretative Learning (MIL). ILP is a branch of Machine Learning where the Artificial Intelligence tries to induce Horn clauses from a given background knowledge and some positive/negative examples.
The goal of this thesis is the development of a system for assisting interpretative learning algorithms. In order to achieve that, we exploit 2p-kt, an ecosystem for Symbolic Artificial Intelligence, providing an extension of the latter for supporting the concept of MetaRule and we design and implement a system of pluggable components aiming to assist the various steps of ILP algorithms (such as generalization of induced rules and refinement of theories).
The results are: a 2p-kt based library of various generalization, validation and refinement strategies, a brand new algorithm inspired by Metagol (named MetaPatrol) and a test suite. The system poses as a 2p-kt extension supporting the definition of MetaRules, different mechanisms of generalization, the validation and refinement of induced theories as first class mechanisms, as a whole allowing the engineering of multiple strategies of MIL