title: Information Theory and Representation in Associative Word Learning
creator: Burns, Brendan
creator: Sutton, Charles
creator: Morrison, Clayton
creator: Cohen, Paul
subject: Machine Learning
subject: Artificial Intelligence
subject: Robotics
description: A significant portion of early language learning can be viewed as an associative learning problem. We investigate the use of associative language learning based on the principle that words convey Shannon information about the environment. We discuss the shortcomings in representation used by previous associative word learners and propose a functional representation that not only denotes environmental categories, but serves as the basis for activities and interaction with the environment. We present experimental results with an autonomous agent acquiring language.
publisher: Lund University Cognitive Studies
contributor: Prince, Christopher G.
contributor: Berthouze, Luc
contributor: Kozima, Hideki
contributor: Bullock, Daniel
contributor: Stojanov, Georgi
contributor: Balkenius, Christian
date: 2003
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/3331/1/Burns.pdf
identifier:   Burns, Brendan and Sutton, Charles and Morrison, Clayton and Cohen, Paul  (2003) Information Theory and Representation in Associative Word Learning.  [Conference Paper]     
relation: http://cogprints.org/3331/