Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for subject:(Quantitative Regret Measure). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters

1. Liao, Zhanrui. Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems.

Degree: MS, Mechanical Engineering, 2017, Clemson University

Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human's rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human's acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks. Advisors/Committee Members: Dr. Yue Wang, Committee Chair, Dr. John Wagner, Dr. Yongqiang Wang.

Subjects/Keywords: Bayesian Sequential Decision-Making; Human-Robot Collaboration; Quantitative Regret Measure; Quantitative Trust Measure

…we first proposed a quantitative measure of regret in HRC systems [37] and then… …future works. 7 Chapter 2 Quantitative Regret Measurements 2.1 A Quantitative Measure of… …Regret-based modified mixed mode . . . . . . . . . . . . . . . . . . . . . . Objective measure… …BSD by integrating regret such that the robot imitates human logic. 1.2.2 Quantitative… …quantitative regret model. If the quantitative model is customized to match the decisionmaking of an… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liao, Z. (2017). Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2651

Chicago Manual of Style (16th Edition):

Liao, Zhanrui. “Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems.” 2017. Masters Thesis, Clemson University. Accessed November 18, 2019. https://tigerprints.clemson.edu/all_theses/2651.

MLA Handbook (7th Edition):

Liao, Zhanrui. “Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems.” 2017. Web. 18 Nov 2019.

Vancouver:

Liao Z. Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems. [Internet] [Masters thesis]. Clemson University; 2017. [cited 2019 Nov 18]. Available from: https://tigerprints.clemson.edu/all_theses/2651.

Council of Science Editors:

Liao Z. Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems. [Masters Thesis]. Clemson University; 2017. Available from: https://tigerprints.clemson.edu/all_theses/2651

.