Data Mining for Social Robotics: Toward Autonomously Social Robots (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.

本書探討了一種基於自主無監督技術的社交機器人方法，並將其置於心理學、神經科學、HRI和數據挖掘相關研究的結構性陳述中。作者們提出了一種自主和發展性的方法，通過使用時間序列分析和機器學習算法，使機器人能夠通過模仿人類來學習互動行為。

第一部分提供了對時間序列分析、變點發現、主題發現和因果分析的全面結構性介紹，重點關注其在HRI問題中的應用可能性。詳細解釋了所涉及的所有算法，並提供了在MATLAB中的開源實現，讓讀者可以進行實驗。在所提出的方法中，模仿和模擬是實現社交行為自主性的關鍵技術。第二部分為讀者提供了心理學和行為學領域中這些領域的研究概述。基於這個背景，作者們討論了賦予機器人自主學習如何成為社交的能力的方法。

《社交機器人的數據挖掘》對於對社交和發展性機器人感興趣的研究生和從業人員來說是必讀之書。