This paper discusses a general framework called FLARE, that integrates inductive learning using prior knowledge together with reasoning in a non-recursive, propositional setting. FLARE learns incrementally by continually revising its knowledge base in the light of new evidence. Prior knowledge is generally given by a teacher and takes the form of pre-encoded rules. Simple defaults, combined with similarity-based reasoning and learning capabilities, enable FLARE to exhibit reasoning that is normally considered non-monotonic. The framework is particularly useful in the context of knowledge acquisition and discovery, as theory and experience are combined. Results of several experiments are reported to demonstrate FLARE's applicability.