Abstract

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 domainindependent general form. We then present a full implementation in a complex domain, namely simulated robotic soccer. 1. Introduction Machine learning (ML) algorithms select a hypothesis from a hypothesis space based on a set of training examples such that the chosen hypothesis is predicted to characterize unseen examples...

Citations