Learning First Order Theories

In the last decade, many efforts have been devoted to the exploration of techniques for learning and refining first order theories, as the necessary step for applying machine learning methodologies to real world applications. In this paper, we present a new approach to the integration of inductive and deductive learning techniques that seems to overcome some of the limitations of existing learning systems without imposing strong constraints or biases on both the representation language and the search space. In particular, a new search structure that enables the system to learn a structured knowledge base is proposed. Moreover, the learning system described in the paper can be used both to learn new knowledge from scratch and to refine an existing background theory.