國立虎尾科技大學 |

Data Mining for Social Robotics = Toward Autonomously Social Robots /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data Mining for Social Robotics/ by Yasser Mohammad, Toyoaki Nishida.
其他題名:	Toward Autonomously Social Robots /
作者:	Mohammad, Yasser.
其他作者:	Nishida, Toyoaki.
面頁冊數:	XII, 328 p. 74 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-319-25232-2
ISBN:	9783319252322

Data Mining for Social Robotics = Toward Autonomously Social Robots /
Mohammad, Yasser.

Data Mining for Social RoboticsToward Autonomously Social Robots /[electronic resource] :by Yasser Mohammad, Toyoaki Nishida. - 1st ed. 2015. - XII, 328 p. 74 illus.online resource. - Advanced Information and Knowledge Processing,1610-3947. - Advanced Information and Knowledge Processing,.

Preface -- 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.

This 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. .

ISBN: 9783319252322

Standard No.: 10.1007/978-3-319-25232-2doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Data Mining for Social Robotics = Toward Autonomously Social Robots /
LDR:03207nam a22004215i 4500 001 969869
003 DE-He213
005 20200702062414.0
007 cr nn 008mamaa
008 201211s2015 gw | s |||| 0|eng d
020 $a 9783319252322 $9 978-3-319-25232-2
024 7 $a 10.1007/978-3-319-25232-2 $2 doi
035 $a 978-3-319-25232-2
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Mohammad, Yasser. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1070615
245 1 0 $a Data Mining for Social Robotics $h [electronic resource] : $b Toward Autonomously Social Robots / $c by Yasser Mohammad, Toyoaki Nishida.
250 $a 1st ed. 2015.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a XII, 328 p. 74 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Advanced Information and Knowledge Processing, $x 1610-3947
505 0 $a Preface -- 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 $a This 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 $a Data mining. $3 528622
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Nishida, Toyoaki. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 675665
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319252308
776 0 8 $i Printed edition: $z 9783319252315
776 0 8 $i Printed edition: $z 9783319797557
830 0 $a Advanced Information and Knowledge Processing, $x 1610-3947 $3 1254273
856 4 0 $u https://doi.org/10.1007/978-3-319-25232-2
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)