Traditional Machine Learning approaches are based on single inference mechanisms. A step forward concerned the integration
of multiple inference strategies within a first-order logic learning framework, taking advantage of the benefits that each
approach can bring. Specifically, abduction is exploited to complete the incoming information in order to handle cases of
missing knowledge, and abstraction is exploited to eliminate superfluous details that can affect the performance of a learning
system. However, these methods require some background information to exploit the specific inference strategy, that must be
provided by a domain expert.
This work proposes algorithms to automatically discover such an information in order to make the learning task completely
autonomous. The proposed methods have been tested on the system INTHELEX, and their effectiveness has been proven by experiments
in a real-world domain.