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Data Mining for Social Robotics [electronic resource] : Toward Autonomously Social Robots / by Yasser Mohammad, Toyoaki Nishida.

By: Mohammad, Yasser [author.].
Contributor(s): Nishida, Toyoaki [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Advanced Information and Knowledge Processing: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015.Description: XII, 328 p. 74 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319252322.Subject(s): Computer science | Data mining | Artificial intelligence | Computer Science | Data Mining and Knowledge Discovery | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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. .
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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. .

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