000 03497nam a22005055i 4500
001 978-3-319-25232-2
003 DE-He213
005 20200421112224.0
007 cr nn 008mamaa
008 160108s2015 gw | s |||| 0|eng d
020 _a9783319252322
_9978-3-319-25232-2
024 7 _a10.1007/978-3-319-25232-2
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aMohammad, Yasser.
_eauthor.
245 1 0 _aData Mining for Social Robotics
_h[electronic resource] :
_bToward Autonomously Social Robots /
_cby Yasser Mohammad, Toyoaki Nishida.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXII, 328 p. 74 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aNishida, Toyoaki.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319252308
830 0 _aAdvanced Information and Knowledge Processing,
_x1610-3947
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-25232-2
912 _aZDB-2-SCS
942 _cEBK
999 _c57556
_d57556