Robot baby 2001

Abstract

Abstract. In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers. We discuss several kinds of meaning that repre-sentations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Specically, a representation is mean-ingful if it incorporates an indicator of external conditions and if the indicator relation informs action. We survey methods for inducing kinds of representations we call structural abstractions. Prototypes of sensory time series are one kind of structural abstraction, and though they are not denoting or compositional, they do support planning. Deictic rep-resentations of objects and prototype representations of words enable a program to learn the denotational meanings of words. Finally, we discuss two algorithms designed to nd the macroscopic structure of episodes in a domain-independent way.