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

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.

[1]  Michael A. Goodrich,et al.  Human-Robot Interaction: A Survey , 2008, Found. Trends Hum. Comput. Interact..

[2]  George Forman,et al.  Learning from Little: Comparison of Classifiers Given Little Training , 2004, PKDD.

[3]  Phil Husbands,et al.  Evolutionary robotics , 2014, Evolutionary Intelligence.

[4]  Pieter Spronck,et al.  Adaptive game AI , 2005 .

[5]  E. Koff,et al.  Facial asymmetry in posed and spontaneous expressions of emotion , 1983, Brain and Cognition.

[6]  Paul Whitney,et al.  Bayesian Networks for Social Modeling , 2011, SBP.

[7]  Nathan F. Lepora,et al.  Naive Bayes texture classification applied to whisker data from a moving robot , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[8]  Mohan Sridharan,et al.  Knowledge Acquisition with Selective Active Learning for Human-Robot Interaction , 2015, HRI.

[9]  Mitsuo Kawato,et al.  Single trial learning of external dynamics: What can the brain teach us about learning mechanisms? , 2007 .

[10]  Jan Peters,et al.  Model learning for robot control: a survey , 2011, Cognitive Processing.

[11]  Hanafiah Yussof,et al.  Humanoid robot NAO: Review of control and motion exploration , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[12]  Kerstin Dautenhahn,et al.  Socially intelligent robots: dimensions of human–robot interaction , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[13]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[14]  Päivi Elisabet Haapasaari,et al.  Integration of biological, economic, and sociological knowledge by Bayesian belief networks: the interdisciplinary evaluation of potential management plans for Baltic salmon , 2011 .

[15]  Tamim Asfour,et al.  A Novel Culture-Dependent Gesture Selection System for a Humanoid Robot Performing Greeting Interaction , 2014, ICSR.

[16]  Ehud Sharlin,et al.  Designing social greetings in human robot interaction , 2014, Conference on Designing Interactive Systems.

[17]  Tamim Asfour,et al.  ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[18]  Michael Izbicki,et al.  Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training , 2013, ICML.

[19]  Elena Torta,et al.  Evaluation of Unimodal and Multimodal Communication Cues for Attracting Attention in Human–Robot Interaction , 2015, Int. J. Soc. Robotics.

[20]  Jan Peters,et al.  Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..

[21]  Michael I. Jordan,et al.  Learning from Incomplete Data , 1994 .

[22]  J. Demiris,et al.  Human-robot-communication and Machine Learning Abbr. Title: Human-robot-communication and Ml , 1997 .

[23]  D. Feil-Seifer,et al.  Defining socially assistive robotics , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[24]  San Cristóbal Mateo,et al.  The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .

[25]  D. Keltner,et al.  Culture and Facial Expression: Open-ended Methods Find More Expressions and a Gradient of Recognition , 1999 .

[26]  Stefano Nolfi,et al.  Evolutionary robotics , 1998, Lecture Notes in Computer Science.

[27]  Elena Torta,et al.  How Can a Robot Attract the Attention of Its Human Partner? A Comparative Study over Different Modalities for Attracting Attention , 2012, ICSR.

[28]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[29]  Bojan Cestnik,et al.  Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.

[30]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[31]  Anthony G. Pipe,et al.  Naive Bayes novelty detection for a moving robot with whiskers , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.

[32]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[33]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[34]  Thomas J. Watson,et al.  An empirical study of the naive Bayes classifier , 2001 .

[35]  Jaap Ham,et al.  Study on Adaptation of Robot Communication Strategies in Changing Situations , 2015, ICSR.

[36]  Jules Thibault,et al.  Process modeling with neural networks using small experimental datasets , 1999 .

[37]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[38]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[39]  Chih-Jen Lin,et al.  Large Linear Classification When Data Cannot Fit in Memory , 2011, TKDD.

[40]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[41]  Yanxi Liu,et al.  The role of structural facial asymmetry in asymmetry of peak facial expressions , 2006, Laterality.