Robots that Learn Meanings

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 representations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Speci cally, a representation is meaningful 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 a kind of planning. Deictic representations of objects and prototype representations of words enable a program to learn the denotational meanings of words. We discuss the implementations of these algorithms on a Pioneer 2 mobile robot.