TY - JOUR
T1 - Application of the Naive Bayes Classifier for Representation and Use of Heterogeneous and Incomplete Knowledge in Social Robotics
AU - Trovato, Gabriele
AU - Chrupala, Grzegorz
AU - Takanishi, Atsuo
PY - 2016/2/22
Y1 - 2016/2/22
N2 - 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.
AB - 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.
KW - social robotics
KW - statistical learning
KW - human-robot interaction
KW - adaptive robotics
KW - incomplete knowledge
U2 - 10.3390/robotics5010006
DO - 10.3390/robotics5010006
M3 - Article
VL - 5
JO - Robotics
JF - Robotics
IS - 1
ER -