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A comparison of machine learning techniques for modeling human-robot interaction with children with autism

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3 Author(s)
Short, E. ; Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA ; Feil-Seifer, D. ; Mataric, M.

Several machine learning techniques are used to model the behavior of children with autism interacting with a humanoid robot, comparing a static model to a dynamic model using hand-coded features. Good accuracy (over 80%) is achieved in predicting child vocalizations; directions for future approaches to modeling the behavior of children with autism are suggested.

Published in:

Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on

Date of Conference:

8-11 March 2011