Special Issue on Human-aligned Reinforcement Learning for Autonomous Agents and Robots @NCAA journal

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Francisco Cruz

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Sep 7, 2021, 10:32:39 AM9/7/21
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** Call for papers ** 
Topical Collection on Human-aligned Reinforcement Learning for Autonomous Agents and Robots at the Springer journal Neural Computing and Applications.


** Topics **
The main topics of interest in the call for submissions are explainability, interactivity, safety, and ethics in social robotics and autonomous agents, especially from a reinforcement learning perspective. In this regard, approaches with special interest for this topical collection are (but not limited to):

- Explainability, interpretability, and transparency methods for feature-oriented and goal-driven RL.
- Explainable robotic systems with RL approaches.
- Assisted and interactive RL in human-robot and human-agent scenarios.
- Human-in-the-loop RL and applications.
- RL from demonstrations and imperfect demonstrations.
- Robot and agent learning from multiple human sources.
- Multi-robot systems with human collaboration.
- Safe exploration during learning.
- Ethical reasoning and moral uncertainty.
- Fairness in RL and multi-agent systems.
- Theory of mind based RL frameworks.
- Use of human priors in RL.

** Provisional deadlines **
- Deadline for submissions: December 15, 2021
- Deadline for review: February 15, 2022
- Decisions: March 15, 2022
- Revised manuscript submission: May 15, 2022
- Deadline for second review: June 15, 2022
- Final decisions: June 30, 2022

** Guest editors **
Dr. Francisco Cruz (Lead guest editor)
School of Information Technology
Deakin University, Australia

Dr. Thommen George Karimpanal
Applied Artificial Intelligence Institute (A2I2)
Deakin University, Australia 

Dr. Miguel Solis, 
Facultad de Ingenieria
Universidad Andres Bello, Chile

Dr. Pablo Barros
Cognitive Architecture for Collaborative Technologies Unit
Italian Institute of Technology (IIT), Italy

A/Prof. Richard Dazeley
School of Information Technology
Deakin University, Australia

** More details at: ** 

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