title: Influencing Robot Learning Through Design and Social Interactions: A Balancing Framework creator: Marom, Yuval creator: Haynes, Gillian subject: Machine Learning subject: Artificial Intelligence subject: Robotics description: 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. publisher: Lund University Cognitive Studies contributor: Berthouze, Luc contributor: Kozima, Hideki contributor: Prince, Christopher G. contributor: Sandini, Giulio contributor: Stojanov, Georgi contributor: Metta, Giorgio contributor: Balkenius, Christian date: 2004 type: Conference Paper type: PeerReviewed format: application/pdf identifier: http://cogprints.org/4072/1/marom.pdf identifier: Marom, Yuval and Haynes, Gillian (2004) Influencing Robot Learning Through Design and Social Interactions: A Balancing Framework. [Conference Paper] relation: http://cogprints.org/4072/