Persistence of seven forage legumes under three grazing regimes / by James Pierre Muir.

Muir, James P.

doi:10.5962/bhl.title.46570

Cited by 1 publication

(4 citation statements)

References 16 publications

(25 reference statements)

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“…• Perceived Dependability: we used the item from Muir questionnaire to what extent can you count on Panda to do its job? [Muir 1989; as cited in Desai (2012)]. • Perceived Reliability: we used the item from Charalambous questionnaire the robot did not seem reliable (Charalambous et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…• Perceived Predictability: we used the item from Muir questionnaire to what extent can the robot's behavior be predicted? [Muir 1989; as cited in Desai (2012)].…”

Section: Methodsmentioning

confidence: 99%

“…• Faith: we used the item from Muir questionnaire to what extent do you believe that the robot will be able to cope with all situations in the future? [Muir 1989; as cited in Desai (2012)]. • For trust itself, we used also the item from Muir questionnaire overall how much do you trust the robot?…”

Section: Methodsmentioning

confidence: 99%

“…• For trust itself, we used also the item from Muir questionnaire overall how much do you trust the robot? [Muir 1989; as cited in Desai (2012)].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Alhaji

Prilla

Rausch

2021

Front. Artif. Intell.

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Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.

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Section: Methodsmentioning

confidence: 99%

“…• Perceived Predictability: we used the item from Muir questionnaire to what extent can the robot's behavior be predicted? [Muir 1989; as cited in Desai (2012)].…”

Section: Methodsmentioning

confidence: 99%