Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction-Reference-Cited by-同舟云学术

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Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction

Published:2020-07-14 Issue:2 Volume:13 Page:381-393
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Zhang Tielin^ORCID,Zeng Yi,Pan Ruihan,Shi Mengting,Lu Enmeng

Funder

Beijing Natural Science Foundation

Young Scientists Fund

the Strategic Priority Research Program of Chinese Academy of Sciences

Beijing Municipality of Science and Technology

CETC Joint Fund

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-020-09753-1.pdf

Reference41 articles.

1. Shen Y-Y, Liu C-L. Incremental adaptive learning vector quantization for character recognition with continuous style adaptation. Cognitive Computation 2018;10(2):334–346.

2. Reyes O, Altalhi AH, Ventura S. Statistical comparisons of active learning strategies over multiple datasets. Knowl-Based Syst 2018;145:274–288.

3. Zhu Z, Hu H. Robot learning from demonstration in robotic assembly: a survey. Robotics 2018;7(2):17.

4. Bhat AA, Mohan V. Goal-directed reasoning and cooperation in robots in shared workspaces: an internal simulation based neural framework. Cognitive Computation 2018;10(4):558–576.

5. Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. 2019. Continual lifelong learning with neuralnetworks: a review. Neural Networks.

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