University of Hertfordshire

By the same authors

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Original languageEnglish
Title of host publicationImitation and Social Learning in Robots, Humans and Animals
Subtitle of host publicationBehavioural, Social and Communicative Dimensions
PublisherCambridge University Press
Pages1-18
Number of pages18
ISBN (Electronic)9780511489808
ISBN (Print)9780521845113
DOIs
Publication statusPublished - 1 Jan 2007

Abstract

Social learning, matched behaviour and imitation are important classes of mechanisms whereby knowledge may be transferred between agents (biological, computational or robotic autonomous systems). They comprise key mechanisms necessary for the evolution and development of social intelligence and culture. Researchers from across disciplines have begun coming together to understand these mechanisms with ever more sophisticated models. While the importance of Social Learning has grown increasingly apparent to psychologists, ethologists, philosophers, linguists, cognitive scientists and computer scientists, biologists, anthropologists and roboticists, the workers in the field are often unaware of relevant research by others in other disciplines. Social learning has lacked a rigorous foundation and only very few major interdisciplinary publications have been available on the subject for researchers in artificial intelligence or psychology interested in realizations of the mechanisms they study. By bringing social learning techniques into computer and robotic systems, the door is being opened for numerous applications that will allow the acquisition of skills, programs and behaviours automatically by observation in human–computer interfaces (e.g. Lieberman, 2001), human–robot interaction important in service robotics and other applications where robot assistants or companions need to learn from humans, and industrial applications such as automated factory floors in which new robots can acquire skills by observing the behaviour of other robots or humans. Models from psychology and biology are being validated and extended as scientists from these fields interact with collaborators from sciences of the artificial, while the latter benefit from the insight of their colleagues in the natural and social sciences in the harnessing of social learning in constructed systems.

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