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

Authors:
Elaine Short;David Feil-Seifer;Maja Matarić
Affiliations:
University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA
Venue:
Proceedings of the 6th international conference on Human-robot interaction
Year:
2011

Citing 2
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Abstract

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.