Symbol Grounding With Delay Coordinates

Abstract

"There is nothing more basic than categorization to our thought, perception, action, and speech " (Lakoff 1984). Moreover, categories of sensory experience pro-vide the semantic glue between the world and the mean-ingless symbols often used to represent those experi-ences. As such, the focus of this work is an unsupervised learning mechanism for extracting categories from time series. We have in mind the situation where a sensori-motor agent, such as an infant or mobile robot, records streams of sensor readings while interacting with a com-plex environment. To make the leap from percepts to symbolic thought and language, the agent requires a way of transforming uninterpreted sensor information into meaningful categories. That is, the agent must solve the bottom-up version of the symbol grounding