The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues
Abstract
The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human-agent (H-A) interaction, and retention of human decision authority. A number of approaches-from flexible automation to autonomous agents-were reviewed, and their advantages and disadvantages were discussed. In addition, two key human performance issues (trust and situation awareness) related to H-A teaming for multirobot control and some promising user interface design solutions to address these issues were discussed. Some major individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.
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Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 2×
“acquisition, information analysis, decision making, and action implementation) [68]; they reported that automating the information acquisition part of the task was most beneficial for sup-”
BackgroundMachines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 1×
“Madhavan and Wiegmann [83] suggested, based on the research findings of Nass and Moon [182] on humans’ social responses to computers, that techniques such as anthropomorphizing automation may help individuals better calibrate their trust in the systems.”
BackgroundRempel, J. K., Holmes, J. G., & Zanna, M. P. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.49.1.95 · Cited by 3,146 · cited 1×
“have been numerous definitions of trust in the literature of organizational psychology, interpersonal relationships, and human– machine interactions, among other fields [80], [83]–[85].”
BackgroundMonsell, S. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(03)00028-7 · Cited by 2,984
Conditions for intuitive expertise: A failure to disagree.
Kahneman, D., & Klein, G. (2009) · American Psychologist · doi:10.1037/a0016755 · Cited by 2,443
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811417254 · Cited by 2,051
Self-citationSIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905 · cited 1×
“The most common solution to the PLS-based model with multiple response variables is SIMPLS [60].”
MethodFoundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 7×
“However, staying “on the loop” does not make operators’ jobs easier; as Parasuraman and Manzey [21] noted, automation does not simply perform tasks for humans—it changes the nature of humans’ tasks.”
BackgroundInfluentialControl and interference in task switching—A review
Kiesel, A., Steinhauser, M., Wendt, M., Falkenstein, M., Jost, K., Philipp, A. M., & Koch, I. (2010) · Psychological Bulletin · doi:10.1037/a0019842 · Cited by 1,485
Performance Consequences of Automation-Induced 'Complacency'
Parasuraman, R., Molloy, R., & Singh, I. L. (1993) · The International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0301_1 · Cited by 875 · cited 1×
“degree to which he/she relies (or over relies) on the automation [89], [90].”
BackgroundHuman Performance Issues and User Interface Design for Teleoperated Robots
Chen, J. Y. C., Haas, E. C., & Barnes, M. J. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.905819 · Cited by 646
Self-citationMerritt, S. M., & Ilgen, D. R. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x288574 · Cited by 527
Human Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720813501549 · Cited by 524
Miller, C. A., & Parasuraman, R. (2007) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872007779598037 · Cited by 434
Manzey, D., Reichenbach, J., & Onnasch, L. (2012) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411433844 · Cited by 277
de Vries, P., Midden, C., & Bouwhuis, D. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00039-9 · Cited by 264
Affective Processes in Human–Automation Interactions
Merritt, S. M. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811411912 · Cited by 260
McGuirl, J. M., & Sarter, N. B. (2006) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872006779166334 · Cited by 212
Do people hold a humanoid robot morally accountable for the harm it causes?
Kahn, P. H., Kanda, T., Ishiguro, H., Gill, B. T., Ruckert, J. H., Shen, S., Gary, H. E., Reichert, A. L., Freier, N. G., & Severson, R. L. (2012) · Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction · doi:10.1145/2157689.2157696 · Cited by 178
Individual differences in working memory capacity predict visual attention allocation
Bleckley, M. K., Durso, F. T., Crutchfield, J. M., Engle, R. W., & Khanna, M. M. (2003) · Psychonomic Bulletin & Review · doi:10.3758/bf03196548 · Cited by 156
Lathan, C. E., & Tracey, M. (2002) · Presence: Teleoperators and Virtual Environments · doi:10.1162/105474602760204282 · Cited by 123
Dong, X., & Hayes, C. C. (2012) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411432338 · Cited by 36
Cited by
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816634228 · Cited by 768
Self-citationStephanidis, C., Salvendy, G., Antona, M., Chen, J. Y. C., Dong, J., Duffy, V. G., Fang, X., Fidopiastis, C., Fragomeni, G., Fu, L. P., et al. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1619259 · Cited by 524
Self-citationTowards a Theory of Longitudinal Trust Calibration in Human–Robot Teams
de Visser, E. J., Peeters, M. M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., & Neerincx, M. A. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-019-00596-x · Cited by 447
Intelligent agent transparency in human–agent teaming for multi-UxV management
Mercado, J. E., Rupp, M. A., Chen, J. Y. C., Barnes, M. J., Barber, D., & Procci, K. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720815621206 · Cited by 403
Self-citation
Metadata
- Authors
- Chen, J. Y. C., & Barnes, M. J.
- Venue
- IEEE Transactions on Human-Machine Systems
- Published
- 2014-02
- Volume
- 44
- Issue
- 1
- Pages
- 13-29
- ISSN
- 2168-2291 (ISSN-L)
- 2168-2305
- OpenAlex
- W2069287462
- Cited by
- 522
- References
- 29
- FWCI
- 38.63
- Type
- journal article
- Language
- en
- Open access
- closed
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