Determining Novice and Expert Status in Human–Automation Interaction Through Hidden Markov Models-Reference-Cited by-同舟云学术

Determining Novice and Expert Status in Human–Automation Interaction Through Hidden Markov Models

Published:2024-09-10 Issue:1 Volume:38 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

French Anne¹^ORCID,Cummings Mary L.²,Zhu Haibei¹,Pajic Miroslav¹

Affiliation:

1. Department of Electrical and Computer Engineering, Edmund T. Pratt Jr. School of Engineering, Duke University, Durham, North Carolina, USA

2. College of Engineering and Computing, George Mason University, Fairfax, Virginia, USA

Funder

Air Force Office of Scientific Research

National Science Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2024.2402174

Reference39 articles.

1. An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data

2. Visual scanning and pilot expertise: The role of attentional flexibility and mental model development;Bellenkes A. H.;Aviation, Space, and Environmental Medicine,1997

3. Boussemart, Y., J. Las Fargeas, M. L. Cummings, and N. Roy. 2009. Comparing learning techniques for hidden Markov models of human supervisory control behavior. In AIAA Infotech@ Aerospace Conference and AIAA Unmanned Unlimited Conference, Seattle, WA, USA, April.

4. Cheryl, P., D. M. T. Michael Ingerick, and L. Wasko. 2011. Identifying best bet entry-level selection measures for US air force remotely piloted aircraft (RPA) pilot and sensor operator (SO) occupations. Alexandria, VA: Human Resources Research Organization.

5. Model selection in hidden Markov models: A simulation study;Costa M.;Quaderni di Dipartimento, Department of Statistics, University of Bologna,2010