Reputation systems: A survey and taxonomy

Hendrikx, Ferry; Bubendorfer, Kris; Chard, Ryan

doi:10.1016/j.jpdc.2014.08.004

Cited by 181 publications

(129 citation statements)

References 32 publications

Supporting

Mentioning

115

Contrasting

Unclassified

Order By: Relevance

“…In many domains, trustor agents use trust and reputation to select interaction partners from sets of trustees [HBC15]. Trust can be assessed using direct experience gathered by a trustor interacting with trustee agents.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Stereotype Reputation with Limited Observability

Taylor

Griffiths

Barakat

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In multi-agent systems (MAS) agents must decide whether or not to interact with others, and can use trust and reputation to inform this decision [YSL + 13, HBC15,WBOM15]. Trust is the degree of belief, from the perspective of a trustor agent, that a trustee agent will act as they say they will in a given context [AG07,JIB07,BNS13].…”

Section: Introductionmentioning

confidence: 99%

Stereotype Reputation with Limited Observability

Taylor

Griffiths

Barakat

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Although, a thorough overview of related literature is out of the scope of this article, we tried to catch out parameters, or factors for others, that are usually referred either explicitly or implicitly in reputation models and metrics, e.g. [8,9,16,19,22,49,48]. To this end, DISARM uses for its needs six properties; namely response time, validity, completeness, correctness, cooperation and outcome feeling.…”

Section: Rating Parametersmentioning

confidence: 99%

DISARM: A social distributed agent reputation model based on defeasible logic

Kravari

Bassiliades

2016

Journal of Systems and Software

View full text Add to dashboard Cite

Intelligent Agents act in open and thus risky environments, hence making the appropriate decision about who to trust in order to interact with, could be a challenging process.As intelligent agents are gradually enriched with Semantic Web technology, acting on behalf of their users with limited or no human intervention, their ability to perform assigned tasks is scrutinized. Hence, trust and reputation models, based on interaction trust or witness reputation, have been proposed, yet they often presuppose the use of a centralized authority. Although such mechanisms are more popular, they are usually faced with skepticism, since users may question the trustworthiness and the robustness of a central authority. Distributed models, on the other hand, are more complex but they provide personalized estimations based on each agent's interests and preferences. To this end, this article proposes DISARM, a novel distributed reputation model. DISARM deals MASs as social networks, enabling agents to establish and maintain relationships, limiting the disadvantages of the common distributed approaches. Additionally, it is based on defeasible logic, modeling the way intelligent agents, like humans, draw reasonable conclusions from incomplete and possibly conflicting (thus inconclusive) information. Finally, we provide an evaluation that illustrates the usability of the proposed model.

show abstract

“…Because, even with greater deviation, the use of these bootstrapped reputation values is still better than assigning initial high (maximum), low (minimal), or any fixed (e.g., average) reputation values. Services S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 [2], [4]), a little effort has been dedicated to the study of reputation bootstrapping.…”

Section: Reputation Estimation and Evaluationmentioning

confidence: 99%

Regression-Based Bootstrapping of Web Service Reputation Measurement

Tibermacine

Cherif

2015

2015 IEEE International Conference on Web Services

View full text Add to dashboard Cite

Abstract-In the literature, many solutions for measuring the reputation of web services have been proposed. These solutions help in building service recommendation systems. Nonetheless, there are still many challenges that need to be addressed in this context, such as the "cold start" problem, and the lack of estimation of the initial reputation values of newcomer web services. As reputation measurement depends on the previous reputation values, the lack of initial values can subvert the performance of the whole service recommendation system, making it vulnerable to different threats, like the Sybil attack. In this paper, we propose a new bootstrapping mechanism for evaluating the reputation of newcomer web services based on their initial Quality of Service (QoS) attributes, and their similarity with "long-standing" web services. Basically, the technique uses regression models for estimating the unknown reputation values of newcomer services from their known values of QoS attributes. The technique has been experimented on a large set of services, and its performance has been measured using some statistical metrics, such as the coefficient of determination (R 2 ) and the Mean Square Error (MSE).

show abstract

Reputation systems: A survey and taxonomy

Cited by 181 publications

References 32 publications

Stereotype Reputation with Limited Observability

Stereotype Reputation with Limited Observability

DISARM: A social distributed agent reputation model based on defeasible logic

Regression-Based Bootstrapping of Web Service Reputation Measurement

Contact Info

Product

Resources

About