The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A study measuring the impact of shared decision making in a human-robot team
Abstract
This paper presents the results of a user study in which the impact of sharing decision making in a human-robot team was measured. In the experiments outlined here, a human and robot play a game together in which the robot searches an arena for items, with input from the human, and the human-robot team earns points for finding and correctly identifying the items. The user study reported here involved 60 human subjects. Each subject interacted with two different robots. With one robot, the human acted as a supervisor: the human issued commands and the robot obeyed. With the other robot, the human acted as a collaborator: the human and robot shared decisions and were required to reach agreement about the robot’s actions in the arena before any actions were taken, facilitated using computational argumentation. Objective performance metrics were collected and analyzed for both types of human-robot team, as well subjective feedback from human subjects regarding attitudes toward working with a robot. The objective results showed significant improvement in performance metrics with the human-as-collaborator pairs versus the human-as-supervisor pairs. Subjective results demonstrated significant differences across many subjective measures and indicated a distinct preference for the human-as-collaborator mode. The primary contribution of this work lies in the demonstration and evaluation of a computational argumentation approach to human-robot interaction, particularly in proving the efficacy of this approach over a less autonomous mode of interaction.
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“(I) Higher levels of agent autonomy generally have positive effects (e.g., participant attitudes, workload, and performance), although a moderate level was ideal in a few studies.”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Azhar and Sklar (2017)
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“humans perceived better collaboration, stronger trust, and increased level of task understanding with greater levels of agent autonomy (e.g., Azhar & Sklar, 2017)”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Azhar & Sklar, 2017
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“humans perceived better collaboration, stronger trust, and increased level of task understanding with greater levels of agent autonomy (e.g., Azhar & Sklar, 2017)”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Azhar & Sklar, 2017
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“humans perceived better collaboration, stronger trust, and increased level of task understanding with greater levels of agent autonomy (e.g., Azhar & Sklar, 2017)”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Azhar & Sklar, 2017
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Cites
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“” Some of these survey questions were inspired by the NASA-TLX measurement instrument (Hart and Staveland 1988; Hart 2006), particularly to assess participants’ perceived levels of mental demand and task complexity.”
MethodA 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
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“Although a Likert response format was used in the survey and the data collected is ordinal in nature, there is evidence in the literature defending the robustness of ANOVA for analysing Likert response format data (Glass et al. 1972; Carifio and Perla 2007).”
MethodEffects of adaptive robot dialogue on information exchange and social relations
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Cited by
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Metadata
- Authors
- Azhar, M. Q., & Sklar, E. I.
- Affiliations
- The City University Of New York, USA
- King’s College London, UK
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2017-06
- Volume
- 36
- Issue
- 5-7
- Pages
- 461-482
- ISSN
- 0278-3649
- 1741-3176
- Cited by
- 23
- References
- 7
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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