OPTIMo

Xu, Anqi; Dudek, Gregory

doi:10.1145/2696454.2696492

Cited by 93 publications

(12 citation statements)

References 13 publications

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“…A more general approach is to directly model the human's dynamic trust in the robot. Work in this area has focused on two problems: (i) estimating trust based on observations of the human's behavior [18,27,[57][58][59][60][61][62][63][64] and (ii) utilizing the estimate of trust to guide robot behavior [27,58,[65][66][67][68][69].…”

Section: Computational Trust Modelsmentioning

confidence: 99%

“…A major line of work in this area was started with the introduction of the Online Probabilistic Trust Inference Model (OPTIMo) [61], which captures trust as a latent variable in a dynamic probabilistic graphical model (PGM) [28]. While there have been other pioneering attempts to model trust, they have been restricted to simple functions [60] or fail to account for uncertainty in the trust estimation [59].…”

Section: Computational Trust Modelsmentioning

confidence: 99%

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Trust in Robots: Challenges and Opportunities

2020

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Purpose of Review To assess the state-of-the-art in research on trust in robots and to examine if recent methodological advances can aid in the development of trustworthy robots. Recent Findings While traditional work in trustworthy robotics has focused on studying the antecedents and consequences of trust in robots, recent work has gravitated towards the development of strategies for robots to actively gain, calibrate, and maintain the human user's trust. Among these works, there is emphasis on endowing robotic agents with reasoning capabilities (e.g., via probabilistic modeling). Summary The state-of-the-art in trust research provides roboticists with a large trove of tools to develop trustworthy robots. However, challenges remain when it comes to trust in real-world human-robot interaction (HRI) settings: there exist outstanding issues in trust measurement, guarantees on robot behavior (e.g., with respect to user privacy), and handling rich multidimensional data. We examine how recent advances in psychometrics, trustworthy systems, robot-ethics, and deep learning can provide resolution to each of these issues. In conclusion, we are of the opinion that these methodological advances could pave the way for the creation of truly autonomous, trustworthy social robots.

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Section: Computational Trust Modelsmentioning

confidence: 99%

Section: Computational Trust Modelsmentioning

confidence: 99%

Trust in Robots: Challenges and Opportunities

2020

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“…Xu proposed a dynamic Bayesian inference trust model (Xu & Dudek, 2015). that uses a robot's performance to predict the operator's latent trust state and constructs a Bayesian network to incorporate influences from past time steps.…”

Section: Computational Trust Modelsmentioning

confidence: 99%

“…The result of trust predictions (RMSE). The Kalman filter model is compared with the IRL model proposed in (Nam et al, 2019) and DBN model in (Xu & Dudek, 2015)). Fig.…”

Section: Comparison With Existing Modelsmentioning

confidence: 99%

A Kalman estimation model of human trust in supervisory control of robotic swarms

Li¹,

Lewis

Sycara

2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Trust is an important factor in the interaction between humans and automation that can mediate the reliance of human operators. In this work, we evaluate a computational model of human trust on swarm systems based on Sheridan (2019)’s modified Kalman estimation model using existing experiment data (Nam, Li, Li, Lewis, & Sycara, 2018). Results show that our Kalman Filter model outperforms existing state of the art alternatives including dynamic Bayesian networks and inverse reinforcement learning. This work is novel in that: 1) The Kalman estimator is the first computational model formulating the human trust evolution as a combination of both open-loop trust anticipation and closed-loop trust feedback. 2) The proposed model considers the operator’s cognitive time lag between perceiving and processing the system display. 3) The proposed model provides a personalized model for each individual and reaches a better level of fitness than state-of-the-art alternatives.

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“…Gao, Clare, Macbeth, & Cummings, 2013; Khasawneh, Bowling, Jiang, Gramopadhye, & Melloy, 2003; Xu & Dudek, 2015) found that trust is significantly affected by automation reliability, in terms of the probability that automation performs its assigned tasks correctly. Trust is also of a dynamic nature, as it can drop notably after automation errors (Akash, Hu, Reid, & Jain, 2017; Desai, Kaniarasu, Medvedev, Steinfeld, & Yanco, 2013; Xu & Dudek, 2015). Hence, an accurate estimation of trust requires repeated measurements during an interaction (Schaefer, 2016).…”

Section: Automation Reliability and Trust In Automationmentioning

confidence: 99%

Trust Mediating Reliability–Reliance Relationship in Supervisory Control of Human–Swarm Interactions

2019

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Objective: This work aims to further test the theory that trust mediates the interdependency between automation reliability and the rate of human reliance on automation. Background: Human trust in automation has been the focus of many research studies. Theoretically, trust has been proposed to impact human reliance on automation by mediating the relationship between automation reliability and the rate of human reliance. Experimentally, however, the results are contradicting as some confirm the mediating role of trust, whereas others deny it. Hence, it is important to experimentally reinvestigate this role of trust and understand how the results should be interpreted in the light of existing theory. Method: Thirty-two subjects supervised a swarm of unmanned aerial vehicles (UAVs) in foraging missions in which the swarm provided recommendations on whether or not to collect potential targets, based on the information sensed by the UAVs. By manipulating the reliability of the recommendations, we observed changes in participants’ trust and their behavioral responses. Results: A within-subject mediation analysis revealed a significant mediation role of trust in the relationship between swarm reliability and reliance rate. High swarm reliability increased the rate of correct acceptances, but decreased the rate of correct rejections. No significant effect of reliability was found on response time. Conclusion: Trust is not a mere by-product of the interaction; it possesses a predictive power to estimate the level of reliance on automation. Application: The mediation role of trust confirms the significance of trust calibration in determining the appropriate level of reliance on swarm automation.

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OPTIMo

Cited by 93 publications

References 13 publications

Trust in Robots: Challenges and Opportunities

Trust in Robots: Challenges and Opportunities

A Kalman estimation model of human trust in supervisory control of robotic swarms

Trust Mediating Reliability–Reliance Relationship in Supervisory Control of Human–Swarm Interactions

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