A Unified Bi-directional Model for Natural and Artificial Trust in Human–Robot Collaboration
dc.contributor.author | Azevedo-Sa, Hebert | |
dc.contributor.author | Yang, X. Jessie | |
dc.contributor.author | Robert, Lionel + "Jr" | |
dc.contributor.author | Tilbury, Dawn | |
dc.date.accessioned | 2021-06-03T10:15:52Z | |
dc.date.available | 2021-06-03T10:15:52Z | |
dc.date.issued | 2021-06-03 | |
dc.identifier.citation | Azevedo-Sa, H., Yang, X. J., Robert, L. P. and Tilbury, D. (2021). A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration, IEEE Robotics and Automation Letters, Conditionally Accepted. | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/167859 | en |
dc.description.abstract | We introduce a novel capabilities-based bidirectional multi-task trust model that can be used for trust prediction from either a human or a robotic trustor agent. Tasks are represented in terms of their capability requirements, while trustee agents are characterized by their individual capabilities. Trustee agents’ capabilities are not deterministic; they are represented by belief distributions. For each task to be executed, a higher level of trust is assigned to trustee agents who have demonstrated that their capabilities exceed the task’s requirements. We report results of an online experiment with 284 participants, revealing that our model outperforms existing models for multi-task trust prediction from a human trustor. We also present simulations of the model for determining trust from a robotic trustor. Our model is useful for control authority allocation applications that involve human–robot teams. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE Robotics and Automation Letters | en_US |
dc.subject | Human-Robot Collaboration | en_US |
dc.subject | Human Robot Collaboration | en_US |
dc.subject | Human-Robot Interaction | en_US |
dc.subject | Human Robot Interaction | en_US |
dc.subject | Human-Robot Trust | en_US |
dc.subject | robot trust | en_US |
dc.subject | robotic trustor | en_US |
dc.subject | human–robot teams | en_US |
dc.subject | human robot teams | en_US |
dc.subject | human robot teaming | en_US |
dc.subject | Bi-directional trust | en_US |
dc.subject | human robot trust model | en_US |
dc.subject | capabilities-based bidirectional trust model | en_US |
dc.subject | capabilities based bidirectional multi-task trust model | en_US |
dc.subject | multi-task trust model | en_US |
dc.subject | capabilities-based trust model | en_US |
dc.subject | capabilities based trust model | en_US |
dc.subject | Artificial Trust | en_US |
dc.subject | Natural Trust | en_US |
dc.subject | Teamwork trust | en_US |
dc.subject | multi-task trust prediction | en_US |
dc.subject | human computer interaction | en_US |
dc.subject | artificial intelligence trust | en_US |
dc.subject | trust | en_US |
dc.subject | computer supported collaborative work | en_US |
dc.subject | human robot work | en_US |
dc.subject | robot control authority allocation | en_US |
dc.subject | human robot control authority allocation | en_US |
dc.subject | bi-directional trust | en_US |
dc.subject | robot trust prediction | en_US |
dc.subject | autonomous agent trust | en_US |
dc.subject | human autonomous agent interaction | en_US |
dc.title | A Unified Bi-directional Model for Natural and Artificial Trust in Human–Robot Collaboration | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Information Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationum | Robotics Institute | en_US |
dc.contributor.affiliationum | College of Engineering | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/167859/1/Azevedo-Sa et al. 2021.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/1286 | |
dc.identifier.source | IEEE Robotics and Automation Letters | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.description.filedescription | Description of Azevedo-Sa et al. 2021.pdf : Preprint | |
dc.description.depositor | SELF | en_US |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.working.doi | 10.7302/1286 | en_US |
dc.owningcollname | Information, School of (SI) |
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