  Learning and reasoning are both aspects of what is considered to be
intelligence. Their studies within AI have been separated historically,
learning being the topic of machine learning and neural networks, and reasoning
falling under classical (or symbolic) AI. However, learning and reasoning are
in many ways interdependent. This paper discusses the nature of some of these
interdependencies and proposes a general framework called FLARE, that combines
inductive learning using prior knowledge together with reasoning in a
propositional setting. Several examples that test the framework are presented,
including classical induction, many important reasoning protocols and two
simple expert systems.
