A self-organizing incremental learning model that attempts to combine inductive learning with prior knowledge and default reasoning is described. The inductive learning scheme accounts for useful generalizations and dynamic priority allocation, and effectively supplements prior knowledge. New rules may be created and existing rules modified, thus allowing the system to evolve over time. By combining the extensional and intensional approaches to learning rules, the model remains self-adaptive, while not having to unnecessarily suffer from poor (or atypical) learning environments. By combining rule-based and similarity-based reasoning, the model effectively deals with many aspects of brittleness.