Dynamic real-time scheduling for human-agent collaboration systems based on mutual trust

Xiaotian Wang, Zhenwu Shi, Fumin Zhang and Yue Wang

Cyber-Physical Systems, 2015, vol. 1, issue 2-4, 76-90

Abstract: Mutual trust is a key factor in human–human collaboration. Inspired by this social interaction, we analyse human–agent mutual trust in the collaboration of human and (semi)autonomous multi-agent systems. Human–agent mutual trust should be bidirectional and determines the human’s acceptance and hence use of autonomous agents as well as agents’ willingness to take human’s command. It is especially important when a human collaborates with multiple agents concurrently. In this paper, we derive time-series human–agent mutual trust models based on results from human factors engineering. To avoid both ‘over-trust’ and ‘under-trust’, we set up dynamic timing models for the multi-agent scheduling problem and develop necessary and sufficient conditions to test the schedulability of the human multi-agent collaborative task. Our simulation results show that the proposed algorithm guarantees effective real-time scheduling of the human multi-agent collaboration system while ensuring a proper level of human–agent mutual trust.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23335777.2015.1056755 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tcybxx:v:1:y:2015:i:2-4:p:76-90

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tcyb20

DOI: 10.1080/23335777.2015.1056755

Access Statistics for this article

Cyber-Physical Systems is currently edited by Yang Xiao

More articles in Cyber-Physical Systems from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().