Supporting Trust Assessment and Decision Making in Coalitions

Burnett, Chris; Norman, Timothy J.; Sycara, Katia; Oren, Nir

doi:10.1109/mis.2013.53

Cited by 17 publications

(24 citation statements)

References 8 publications

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“…Further work by the researchers in [14] shows how stereotypes in MAS can be spread throughout the group to improve others' trust assessments, and can be used by agents to detect unwanted biases received from others in the group. In [15], the authors show how this work can be used by organizations to create decision models based on trust assessments from stereotypes and other historical information about the other agents.…”

Section: Performance-based Interaction: Humans Influencing Robotsmentioning

confidence: 99%

The Role of Trust in Human-Robot Interaction

Lewis

Sycara

Walker

2018

Studies in Systems, Decision and Control

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170

140

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Section: Performance-based Interaction: Humans Influencing Robotsmentioning

confidence: 99%

The Role of Trust in Human-Robot Interaction

Lewis

Sycara

Walker

2018

Studies in Systems, Decision and Control

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170

140

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“…If the provider has changed sub-provider, the past interaction should not be considered relevant to their current reputation 3 . This is a richer way of accounting for sub-provider actions than simply discounting based on position in a delegation chain [4].…”

Section: Unreliable Sub-providermentioning

confidence: 99%

“…In dynamic environments, where social relationships evolve and the population changes, it can be difficult to assess reputation as there may be a lack of evidence [1,7,8,14]. Stereotypes provide a useful bootstrapping mechanism, but there needs to be a sufficient evidence base from which to induce a prediction model [1,3,14,18] Where there is little data for assessing reputation, individual pieces of evidence can carry great weight and, where negative, may cause a provider rarely to be selected, and never be given the opportunity to build their reputation.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Mitigating Circumstances into Reputation Assessment

Miles

Griffiths

2015

Communications in Computer and Information Science

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Abstract. Reputation enables customers to select between providers, and balance risk against other aspects of service provision. For new providers that have yet to establish a track record, negative ratings can significantly impact on their chances of being selected. Existing work has shown that malicious or inaccurate reviews, and subjective differences, can be accounted for. However, an honest balanced review of service provision may still be an unreliable predictor of future performance if the circumstances differ. Specifically, mitigating circumstances may have affected previous provision. For example, while a delivery service may generally be reliable, a particular delivery may be delayed by unexpected flooding. A common way to ameliorate such effects is by weighting the influence of past events on reputation by their recency. In this paper, we argue that it is more effective to query detailed records of service provision, using patterns that describe the circumstances to determine the significance of previous interactions.

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“…Agents cannot take for granted that other agents share the same core beliefs about the system or that other agents make accurate statements regarding their competencies and abilities. In addition, agents must accept the possibility that others may intentionally spread false information, or otherwise behaving in a harmful way, to meet their own aims [3]. Therefore, trust evaluating agents, also referred to as trusters (TRs), should use a trust estimation model that allows them to recognize reliable partners in their systems.…”

Section: Introductionmentioning

confidence: 99%

“…TRs use trustworthiness estimation to resolve some of the uncertainty in their interactions and form expectations about the behaviors of others [3]. Trust has been defined in many ways in different domains [1].…”

Section: Introductionmentioning

confidence: 99%

A decentralized trustworthiness estimation model for open, multiagent systems (DTMAS)

Aref

Tran

2015

J Trust Manag

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Often in open multiagent systems, agents interact with other agents to meet their own goals. Trust is, therefore, considered essential to make such interactions effective. However, trust is a complex, multifaceted concept and includes more than just evaluating others' honesty. Many trust evaluation models have been proposed and implemented in different areas; most of them focused on algorithms for trusters to model the trustworthiness of trustees in order to make effective decisions about which trustees to select. For this purpose, many trust evaluation models use third party information sources such as witnesses, but slight consideration is paid for locating such third party information sources. Unlike most trust models, the proposed model defines a scalable way to locate a set of witnesses, and combines a suspension technique with reinforcement learning to improve the model responses to dynamic changes in the system. Simulation results indicate that the proposed model benefits trusters while demanding less message overhead.

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Supporting Trust Assessment and Decision Making in Coalitions

Cited by 17 publications

References 8 publications

The Role of Trust in Human-Robot Interaction

The Role of Trust in Human-Robot Interaction

Incorporating Mitigating Circumstances into Reputation Assessment

A decentralized trustworthiness estimation model for open, multiagent systems (DTMAS)

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