Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 144

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Supervisory Control of Multiple Robots: Effects of Imperfect Automation and Individual Differences

Chen, Jessie Y. C., Barnes, Michael J.·2012·Human Factors: The Journal of the Human Factors and Ergonomics Society
DOI 10.1177/0018720811435843
Open this paper in CoChat — free account

Abstract

Objective: A military multitasking environment was simulated to examine the effects of an intelligent agent, RoboLeader, on the performance of robotics operators. Background: The participants’ task was to manage a team of ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes on the basis of battlefield developments. Method: In the first experiment, RoboLeader was perfectly reliable; in the second experiment, RoboLeader’s recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers. Results: RoboLeader, when perfectly reliable, was helpful in reducing the overall mission times. The type of RoboLeader imperfection (false-alarm vs. miss prone) affected operators’ performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density (clutter of the visual scene) for multiple performance measures. Participants’ attentional control and video gaming experience affected their overall multitasking performance. In both experiments, participants with greater spatial ability consistently outperformed their low-spatial-ability counterparts in tasks that required effective visual scanning. Conclusion: Intelligent agents, such as RoboLeader, can benefit the overall human-robot teaming performance. However, the effects of type of agent unreliability, tasking requirements, and individual differences have complex effects on human-agent interaction. Application: The current results will facilitate the implementation of robots in military settings and will provide useful data to designs of systems for multirobot control.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/0018720811435843

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 8 references

Research works printed but not linked: 50

  1. Barber D., 2008, Paper presented at the 26th Army Science Conference
  2. Barnes M. J., 2010, Human-robot interactions in future military operations, 9
  3. 10.1207/s15327043hup1903_4 · DOI
  4. 10.1177/154193120905300441 · doi:10.1177/154193120905300441
  5. Chappelle W., 2010, Aviation, Space, and Environmental Medicine, 81, 339
  6. Chappelle W., 2010, Aviation, Space, and Environmental Medicine, 81, 253
  7. 10.1002/(SICI)1097-4571(2000)51:6<499::AID-ASI2>3.0.CO;2-K · doi:10.1002/(sici)1097-4571(2000)51:6<499::aid-asi2>3.0.co;2-k
  8. 10.1080/00140139.2010.500404 · DOI
  9. 10.1109/TSMCC.2010.2056682 · DOI · Paper page
  10. 10.1177/1071181311551312 · DOI · Paper page
  11. 10.1080/08995600802115904 · DOI
  12. 10.1080/08995600802565785 · DOI
  13. 10.1080/00140130802030706 · DOI
  14. 10.1080/00140130802680773 · DOI
  15. 10.1109/TSMCA.2005.850587 · DOI · Paper page
  16. 10.1177/0018720810368674 · DOI
  17. 10.1037/0021-843X.111.2.225 · DOI · Paper page
  18. 10.1518/001872006778606822 · DOI · Paper page
  19. 10.1518/001872007X215656 · DOI · Paper page
  20. 10.1016/j.neuron.2009.06.005 · DOI · Paper page
  21. 10.1207/S15327876MP1303_2 · DOI
  22. Ekstrom R. B., 1976, Kit of factor-referenced cognitive tests
  23. 10.1037/0033-2909.130.4.553 · DOI · Paper page
  24. 10.1177/1071181311551087 · DOI
  25. 10.1016/j.cognition.2005.10.004 · DOI · Paper page
  26. Green D., 1988, Signal detection theory and psychophysics
  27. 10.1037/1076-898X.10.2.75 · DOI
  28. 10.1002/acp.1624 · DOI
  29. 10.1002/wcs.116 · DOI
  30. 10.1037/h0035426 · DOI
  31. 10.1177/0018720810366859 · DOI
  32. 10.1177/154193120605002304 · DOI
  33. McKinley A., 2010, Proceedings of the Aerospace Medical Association 81st Annual Scientific Meeting, 336
  34. 10.1518/001872001775870395 · DOI
  35. 10.1518/hfes.46.2.196.37335 · DOI · Paper page
  36. 10.1177/1071181311551191 · DOI
  37. Rodes W., 2005, Poster presented at the Human Factors of UAVs Workshop
  38. 10.1037/0096-1523.27.4.763 · DOI · Paper page
  39. 10.1111/1467-9280.00318 · DOI · Paper page
  40. Schurr N. (2007). Toward human-multiagent teams (Unpublished dissertation). University of Southern California, Los Angeles.
  41. 10.1038/scientificamerican0710-56 · doi:10.1038/scientificamerican0710-56
  42. 10.1109/CTS.2010.5478467 · doi:10.1109/cts.2010.5478467
  43. 10.1080/00140139.2010.489969 · DOI
  44. 10.1080/00140139508925181 · DOI
  45. 10.1145/1349822.1349871 · DOI
  46. 10.1177/154193120404800148 · doi:10.1177/154193120404800148
  47. Triplett J. (2008). The effects of commercial video game playing: A comparison of skills and abilities for the Predator UAV (Unpublished thesis). U.S. Air Force–Air University, Montgomery, AL.
  48. 10.1109/ROMAN.2002.1045607 · doi:10.1109/roman.2002.1045607
  49. 10.1177/1071181311551190 · DOI
  50. 10.1080/00140130600980789 · DOI · Paper page

Cited by

Metadata

Authors
Chen, J. Y. C., & Barnes, M. J.
Affiliations
U.S. Army Research Laboratory, Orlando, Florida
Venue
Human Factors: The Journal of the Human Factors and Ergonomics Society
Publisher
SAGE Publications
Published
2012-02-13
Volume
54
Issue
2
Pages
157-174
ISSN
  • 0018-7208
  • 1547-8181
DOI
10.1177/0018720811435843
Cited by
144
References
8
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.