Data Mining for Social Robotics
Language: English
Published by Springer International Publishing AG, CH, 2016
Series: Book 49 of 66 - Advanced Information and Knowledge Processing
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
£ 120.96
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Add to basketItem description from seller
Seller Inventory # LU-9783319252308
- Title
- Data Mining for Social Robotics
- Author
- Yasser Mohammad, Toyoaki Nishida
- Publisher
- Springer International Publishing AG, CH
- Publication year
- 2016
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 3319252305
- ISBN 13
- 9783319252308
- Edition
- 1st ed. 2015.
- Item weight
- 816 grams
- Dimensions
- 15.6 x 2.06 x 23.39 cm
- Series
- Book 49 of 66: Advanced Information and Knowledge Processing
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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London, United Kingdom
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