Application of the naive bayes classifier for representation and use of heterogeneous and incomplete knowledge in social robotics

Gabriele Trovato, Grzegorz Chrupala, Atsuo Takanishi

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Article number6
JournalRobotics
Volume5
Issue number1
DOIs
Publication statusPublished - 2016 Mar 1

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Naive Bayes Classifier
Robotics
Classifiers
Robot
Robots
Human-robot Interaction
Human robot interaction
Anatomy
Cognition
Choose
Methodology
Modeling
Experiment
Knowledge
Experiments
Human
Model

Keywords

  • Adaptive robotics
  • Human-robot interaction
  • Incomplete knowledge
  • Social robotics
  • Statistical learning

ASJC Scopus subject areas

  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence

Cite this

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