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Autor(en): 
  • Toyoaki Nishida
  • Yasser Mohammad
  • Data Mining for Social Robotics: Toward Autonomously Social Robots 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Februar 2016  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / B / computer science / Data Mining / Data Mining and Knowledge Discovery / Expert systems / knowledge-based systems / Künstliche Intelligenz / Wissensbasierte Systeme, Expertensysteme
    ISBN:  9783319252308 
    EAN-Code: 
    9783319252308 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Advanced Information and Knowledge Processing  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  6328 gr 
    Seiten:  328 
    Illustration:  XII, 328 p. 74 illus. in color., farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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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