JST-CREST / IEEE-RAS Spring School on "Social and Artificial Intelligence for User-Friendly Robots"

17-24 March 2019, Shonan Village, Japan

Invited Speakers

  • Yukie Nagai, National Institute of Information and Communications Technology (NICT), Japan
  • Yukie Nagai Title: Development of social cognition in robots
    Abstract : My talk presents computational models for robots to acquire social cognitive abilities as human infants do. A theoretical framework called predictive coding suggests that the human brain works as a predictive machine, that is, it tries to minimize prediction error, which is calculated as a difference between bottom-up sensory signals and top-down prediction. My talk shows how neural networks based on predictive coding enable robots to learn to generate own actions, estimate the goal of others' actions, imitate them, and help others.

    Bio: Dr. Yukie Nagai is a Senior Researcher, National Institute of Information and Communications Technology. She has been investigating underlying neural mechanisms for social cognitive development by means of computational approach. She designs neural network models for robots to learn to acquire cognitive functions such as self-other cognition, estimation of others' intention and emotion, altruism, and so on based on her theory of predictive learning. The simulator reproducing atypical perception in autism spectrum disorder (ASD) greatly impacts on the society as it enables people with and without ASD to better understand potential causes for social difficulties. She is the research director of JST CREST Cognitive Mirroring since December 2016.

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