Layered Learning is a hierarchical machine learning paradigm designed for domains that are too complex to learn straight from inputs to outputs. It relies on a given subtask decomposition. Learning in each layer directly enables learning at the next higher up layer in a variety of possible ways. Layered Learning was the topic of Peter Stone's Ph.D. thesis.