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Robotics 2016, 5(1), 6; doi:10.3390/robotics5010006

Application of the Naive Bayes Classifier for Representation and Use of Heterogeneous and Incomplete Knowledge in Social Robotics

1
Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 162-0044, Japan
2
Department of Communication and Information Sciences, Tilburg School of Humanities, Tilburg University, Tilburg PO Box 90153, 5000 LE, The Netherlands
3
Department of Modern Mechanical Engineering, Waseda University; Humanoid Robotics Institute (HRI), Waseda University, Tokyo 162-8480, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Huosheng Hu
Received: 27 September 2015 / Revised: 18 January 2016 / Accepted: 14 February 2016 / Published: 22 February 2016
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Abstract

As societies move towards integration of robots, it is important to study how robots can use their cognition in order to choose effectively their actions in a human environment, and possibly adapt to new contexts. When modelling these contextual data, it is common in social robotics to work with data extracted from human sciences such as sociology, anatomy, or anthropology. These heterogeneous data need to be efficiently used in order to make the robot adapt quickly its actions. In this paper we describe a methodology for the use of heterogeneous and incomplete knowledge, through an algorithm based on naive Bayes classifier. The model was successfully applied to two different experiments of human-robot interaction. View Full-Text
Keywords: social robotics; statistical learning; human-robot interaction; adaptive robotics; incomplete knowledge social robotics; statistical learning; human-robot interaction; adaptive robotics; incomplete knowledge
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Trovato, G.; Chrupała, G.; Takanishi, A. Application of the Naive Bayes Classifier for Representation and Use of Heterogeneous and Incomplete Knowledge in Social Robotics. Robotics 2016, 5, 6.

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