This paper presents layered learning, a hierarchical machine learning paradigm. Layered learning applies to tasks for which learning a direct mapping from inputs to outputs is intractable with existing learning algorithms. Given a hierarchical task decomposition into subtasks, layered learning seamlessly integrates separate learning at each subtask layer. The learning of each subtask directly facilitates the learning of the next higher subtask layer by determining at least one of three of its components: (i) the set of training examples; (ii) the input representation; and/or (iii) the output representation. We introduce layered learning in its domain-independent general form. We then present a full implementation in a complex domain, namely simulated robotic soccer.

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In Ramon Lopez de Mantaras and Enric Plaza, editors, Machine Learning: ECML 2000 (Proceedings of the Eleventh European Conference on Machine Learning), 369-381, Barcelona,Catalonia,Spain, May/June 2000. Springer Verlag.

@InCollection{ECML2000, title={Layered Learning}, author={Peter Stone and Manuela Veloso}, booktitle={Machine Learning: ECML 2000 (Proceedings of the Eleventh European Conference on Machine Learning)}, month={May/June}, editor={Ramon Lopez de Mantaras and Enric Plaza}, address={Barcelona,Catalonia,Spain}, publisher={Springer Verlag}, pages={369-381}, url="http://nn.cs.utexas.edu/?ECML2000", year={2000} }