creators_name: Marom, Yuval
creators_name: Haynes, Gillian
editors_name: Berthouze, Luc
editors_name: Kozima, Hideki
editors_name: Prince, Christopher G.
editors_name: Sandini, Giulio
editors_name: Stojanov, Georgi
editors_name: Metta, Giorgio
editors_name: Balkenius, Christian
type: confpaper
datestamp: 2005-04-14
lastmod: 2011-03-11 08:55:50
metadata_visibility: show
title: Influencing Robot Learning Through Design and Social Interactions: A Balancing Framework
ispublished: pub
subjects: comp-sci-mach-learn
subjects: comp-sci-art-intel
subjects: comp-sci-robot
full_text_status: public
keywords: innate knowledge, acquired knowledge, nature-nurture trade-off, development and ineraction
abstract: We present a framework for addressing a challenging trade-off between influencing the learning of a robot through design and through social interactions. We identify different kinds of influences that a designer can introduce at design time, and that an expert can introduce using social interactions, and we use these to characterise a two-dimensional design space. As well as discussing how the two sources of influence affect each other, we propose how learning performance typically varies as a result, and present some empirical findings.
date: 2004
date_type: published
volume: 117
publisher: Lund University Cognitive Studies
pagerange: 75-82
refereed: TRUE
citation:   Marom, Yuval and Haynes, Gillian  (2004) Influencing Robot Learning Through Design and Social Interactions: A Balancing Framework.  [Conference Paper]     
document_url: http://cogprints.org/4072/1/marom.pdf