15 October 2020
Knowledge Technology is announcing a special issue on Human-in-the-loop Machine Learning and its Applications in Neural Computing and Applications.
This special issue will offer the opportunity for researchers and practitioners in the diverse fields of robotics to showcase its solutions and applications where human reinforcement feedback would have a positive impact on the training processes. The inclusion of human-in-the-loop (HIL) would allow robots and machine learning models to use both internal and external feedback to speed up the learning process and also improve its performance. In many ways, this could allow the models to learn through their own self-reflection as well as the external input from a human.
Specifically, as a follow-up journal publication of the special session in HIL machine learning in IEEE SMC 2020, extended versions of the accepted papers are mostly welcomed.
Topics of interest include, but are not limited to:
Human Guided Reinforcement Learning
Human-robot Social Interaction
Dialogue Systems with Human-in-the-loop
Interpretable Machine Learning with Human-in-the-loop
Active Learning and Continuous Learning
Learning by Demonstration
Human Factors in HCI/HRI
Deadline for submissions: 31st December 2020
Deadline for review: 28th February 2021
Decisions: 20th March 2021
Deadline for revised version by authors: 20th April 2021
Deadline for 2nd review: 10th May 2021
Final decisions: 20th May 2021
Dr. Joni Zhong (Lead Guest Editor), Nottingham Trent University, UK
Dr. Mark Elshaw, Coventry University, UK
Dr. Yanan Li, Sussex University, UK
Prof. Dr. Stefan Wermter, University of Hamburg, Germany
Prof. Xiaofeng Liu, Hohai University, China
Paper submissions for the special issue should follow the submission format and guidelines. For more information, please visit this link.