Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies-Reference-Cited by-同舟云学术

Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies

Published:2024-09-04 Issue: Volume: Page:1-35
ISSN:1044-7318
Container-title:International Journal of Human–Computer Interaction
language:en
Short-container-title:International Journal of Human–Computer Interaction

Author:

Abbas Ammar N.¹^ORCID,Amazu Chidera W.²^ORCID,Mietkiewicz Joseph³^ORCID,Briwa Houda³,Perez Andres Alonso³,Baldissone Gabriele²^ORCID,Demichela Micaela²^ORCID,Chasparis Georgios C.⁴^ORCID,Kelleher John D.⁵^ORCID,Leva Maria Chiara³^ORCID

Affiliation:

1. Computer Science, Technological University Dublin, Dublin, Ireland

2. Department of Applied Sciences and Technology, Politecnico di Torino, Turin, Italy

3. Food Science and Environmental Health, Technological University Dublin, Dublin, Ireland

4. Data Science, Software Competence Center Hagenberg GmbH, Hagenberg, Austria

5. ADAPT Research Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10447318.2024.2391605

Reference46 articles.

1. Abbas A. N. Chasparis G. C. & Kelleher J. D. (2022a August 28–September 1). Deep residual policy re-inforcement learning as a corrective term in process control for alarm reduction: A preliminary report [Paper presentation]. Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022) Dublin Ireland.

2. Abbas A. N. Chasparis G. C. & Kelleher J. D. (2022b). Interpretable input-output hidden Markov model-based deep reinforcement learning for the predictive maintenance of turbofan engines. In International Conference on Big Data Analytics and Knowledge Discovery (pp. 133–148). Springer.

3. Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance