One-shot learning - Specialization and generalization

Abstract

Most work in connectionist learning, and indeed in the present volume, is devoted to induction from examples, but instruction is as important in symbolic artificial intelligence as it is in nature. This chapter begins our exploration of recruitment learning by examining a connectionist learning system capable of rapidly integrating a new fact into an existing knowledge base in response to a single instruction expressed in a formal description language. Such instructions are transformed into an input for a connectionist knowledge representation system, which in turn changes the network in order to integrate new knowledge. In subsequent chapters we shall consider models based explicitly on neurally plausible circuits, with operations defined at the level of the individual neuron or assemblies of neurons. Here the representation is more abstract, in principle replacing the neural assembly by a single node - demonstrating both the power of recruitment learning as a knowledge acquisition strategy, and the potential alignment between connectionist systems and the established agenda of symbolic artificial intelligence. © 2010 Springer-Verlag Berlin Heidelberg.