Abstract
Traditional Machine Learning approaches based on single inference mechanisms have reached their limits. This causes the need for a framework that integrates approaches based on abduction and abstraction capabilities in the inductive learning paradigm, in the light of Michalski's Inferential Theory of Learning (ITL). This work is intended as a survey of the most significant contributions that are present in the literature, concerning single reasoning strategies and practical ways for bringing them together and making them cooperate in order to improve the effectiveness and efficiency of the learning process. The elicited role of an abductive proof procedure is tackling the problem of incomplete relevance in the incoming examples. Moreover, the employment of abstraction operators based on (direct and inverse) resolution to reduce the complexity of the learning problem is discussed. Lastly, a case study that implements the combined framework into a real multistrategy learning system is briefly presented.