Supervised learning is a cognitive phenomenon which has proved amenable both to theoretical analysis as well as exploitation as a technology. However, not all of cognition can be accounted for directly by supervised learning. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the major component of cognition that needs to be added. We suggest that the central challenge therefore is to unify the formulation of these two phenomena, learning and reasoning, into a single framework with a common semantics. With this one would aim to learn rules with the same success that predicates can be learned. We discuss how Robust Logic fits such a role as a theoretical framework. We also discuss the challenges of testing this experimentally on a usefully significant scale.
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