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 neural networks and machine learning, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning share many interdependencies, and the integration of the two may lead to more powerful models. This dissertation examines some of these interdependencies and describes several models, culminating with a system called FLARE (Framework for Learning And REasoning). The proposed models integrate inductive learning with prior knowledge and reasoning. Learning is incremental, prior knowledge is given by a teacher or deductively obtained by instantiating commonsense knowledge, and reasoning is non-monotonic. Simulation results on several datasets and classical commonsense protocols demonstrate promise.