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008 160108s2015 gw | s |||| 0|eng d
020 _a9783319252322
_z978-3-319-25232-2
024 7 _a10.1007/978-3-319-25232-2
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema006.312
_223
100 1 _aMohammad, Yasser.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aData Mining for Social Robotics :
_bToward Autonomously Social Robots /
_cby Yasser Mohammad, Toyoaki Nishida.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aAdvanced Information and Knowledge Processing,
_x1610-3947
505 0 _aPreface -- Introduction -- Part I: Time Series Mining -- Mining Time-Series Data -- Change Point Discovery -- Motif Discovery -- Causality Analysis -- Part II: Autonomously Social Robots -- Introduction to Social Robotics -- Imitation and Social Robotics -- Theoretical Foundations -- The Embodied Interactive Control Architecture -- Interacting Naturally -- Interaction Learning through Imitation -- Fluid Imitation -- Learning through Demonstration -- Conclusion -- Index.
520 _aThis book explores an approach to social robotics based solely on autonomous unsupervised techniques and positions it within a structured exposition of related research in psychology, neuroscience, HRI, and data mining. The authors present an autonomous and developmental approach that allows the robot to learn interactive behavior by imitating humans using algorithms from time-series analysis and machine learning. The first part provides a comprehensive and structured introduction to time-series analysis, change point discovery, motif discovery and causality analysis focusing on possible applicability to HRI problems. Detailed explanations of all the algorithms involved are provided with open-source implementations in MATLAB enabling the reader to experiment with them. Imitation and simulation are the key technologies used to attain social behavior autonomously in the proposed approach. Part two gives the reader a wide overview of research in these areas in psychology, and ethology. Based on this background, the authors discuss approaches to endow robots with the ability to autonomously learn how to be social. Data Mining for Social Robots will be essential reading for graduate students and practitioners interested in social and developmental robotics. .
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 1 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
700 1 _aNishida, Toyoaki.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _3Springer eBooks
_zOnline access link to the resource
_uhttps://doi.org/10.1007/978-3-319-25232-2
942 _2lcc
_cEBK
041 _aeng