Robots for the people, by the people: Personalizing human-machine interaction-Reference-Cited by-同舟云学术

Robots for the people, by the people: Personalizing human-machine interaction

Published:2018-08-22 Issue:21 Volume:3 Page:
ISSN:2470-9476
Container-title:Science Robotics
language:en
Short-container-title:Sci. Robot.

Author:

Clabaugh Caitlyn¹,Matarić Maja¹^ORCID

Affiliation:

1. Department of Computer Science, University of Southern California, 3710 McClintock Avenue, Los Angeles, CA 90089, USA.

Abstract

Multimodal, interactive, and multitask machine learning can personalize human-machine interaction to a broad diversity of individuals.

Funder

National Science Foundation

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Mechanical Engineering

Reference9 articles.

1. Socially assistive robotics: Human augmentation versus automation

2. T. Baltrusaitis C. Ahuja L.-P. Morency Multimodal machine learning: A survey and taxonomy. IEEE Trans. Pattern Analysis Machine Intelligence 10.1109/TPAMI.2018.2798607 published online 25 January 2018.

3. Mastering the game of Go without human knowledge

4. Power to the people: The role of humans in interactive machine learning;Amershi S.;AI Mag.,2014

5. S. Griffith K. Subramanian J. Scholz C. L. Isbell A. L. Thomaz Policy shaping: Integrating human feedback with reinforcement learning in Advances in Neural Information Processing Systems (Curran Associates Inc. 2013) pp. 2625–2633.

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