An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling

Teacy, W. T. Luke; Lück, Michael; Rogers, Alex; Jennings, Nicholas R.

doi:10.1016/j.artint.2012.09.001

Cited by 104 publications

(84 citation statements)

References 32 publications

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“…In combination, direct-trust and witness-reputation, make up the Beta Reputation System (BRS), as proposed by Jøsang et al [JI02]. Other reputation systems that combine direct-trust and witness-reputation include FIRE [HJS06], TRAVOS [TPJL05], BLADE [RPC06], and HABIT [TLRJ12]. TRAVOS extends BRS to cope with dishonest witnesses by discounting information provided by unreliable sources, and BLADE and HABIT both use Bayesian networks to transform opinions from witnesses that are unreliable in a consistent way.…”

Section: Related Workmentioning

confidence: 99%

Stereotype Reputation with Limited Observability

Taylor

Griffiths

Barakat

et al. 2017

Lecture Notes in Computer Science

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Assessing trust and reputation is essential in multi-agent systems where agents must decide who to interact with. Assessment typically relies on the direct experience of a trustor with a trustee agent, or on information from witnesses. Where direct or witness information is unavailable, such as when agent turnover is high, stereotypes learned from common traits and behaviour can provide this information. Such traits may be only partially or subjectively observed, with witnesses not observing traits of some trustees or interpreting their observations differently. Existing stereotype-based techniques are unable to account for such partial observability and subjectivity. In this paper we propose a method for extracting information from witness observations that enables stereotypes to be applied in partially and subjectively observable dynamic environments. Specifically, we present a mechanism for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability.

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Section: Related Workmentioning

confidence: 99%

Stereotype Reputation with Limited Observability

Taylor

Griffiths

Barakat

et al. 2017

Lecture Notes in Computer Science

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“…We learn a similarity function, given report histories and observable features of agents, and show that this can effectively mitigate the problem of rumour propagation in reputation systems. Teacy et al proposed HABIT, which implicitly estimates trustworthiness of information sources using a hierarchical Bayesian modeling [39]. HABIT exploits similarities between source reports and direct evidence.…”

Section: Related Workmentioning

confidence: 99%

How to trust a few among many

Etuk

Norman

źEnsoy

et al. 2016

Auton Agent Multi-Agent Syst

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The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.

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“…When there is a lack of personal experience, the truster seeks the opinions of other sources, accounting for their reliability. Similarly, HABIT [2] uses a probabilistic approach, utilising Bayesian network to support reasoning about reputation. ReGreT [3] takes into account three dimensions of reputation: the individual dimension (based on direct interactions), the social dimension (from other sources utilising the group relation), and the ontological dimension (defining the different reputational aspects).…”

Section: Related Workmentioning

confidence: 99%

“…Trust and reputation provide an effective way of assessing and managing this risk, and are studied by researchers from many domains. In multi-agent systems, most established computational reputation models, such as TRAVOS [1], HABIT [2], ReGreT [3] and FIRE [4], typically use a combination of direct and indirect experience. In TRAVOS [1], the trust score is the expected probability (using the beta distribution) that the trustee will fulfil its obligations towards the truster in an interaction, estimated based on the outcomes of the previous direct interactions with the trustee.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we will use the terms trust and reputation interchangeably. Many models exist in which reputation is derived from direct experience of clients and third party recommendations, with numerical or probabilistic representations for reputation [1][2][3][4][5][6][7]. However, these existing methods focus solely on the clients' experience of the ultimate outcome of a service provision, without consideration of the full history of activity behind such provision, thus omitting from assessment potentially relevant information.…”

Section: Introductionmentioning

confidence: 99%

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Context-Driven Assessment of Provider Reputation in Composite Provision Scenarios

Barakat

Taylor

Griffiths

et al. 2015

Service-Oriented Computing

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Abstract. Service-oriented computing has become the de-facto way of developing distributed applications and, in such systems, an accurate assessment of reputation is essential for selecting between alternative providers. Existing methods typically assess reputation on a combination of direct experiences by the client being provided with a service and third party recommendations, but they exclude from consideration a wealth of information about the context of providers' previous actions. Such information is particularly important in composite service provision scenarios, where providers may delegate sub-tasks to others, and thus their success or failure needs to be interpreted in this context and reputation assessed according to responsibility. In response, to enable richer, more accurate reputation mechanisms, this paper models the delegation knowledge underlying a composite service provision, and incorporates such knowledge into the reputation assessment process, adjusting the contributions of past interactions with the composite service provider according to delegation context relevance. Experimental results demonstrate the effectiveness of the proposed approach.

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An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling

Cited by 104 publications

References 32 publications

Stereotype Reputation with Limited Observability

Stereotype Reputation with Limited Observability

How to trust a few among many

Context-Driven Assessment of Provider Reputation in Composite Provision Scenarios

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