Trust measures for competitive agents

Rosaci, Domenico

doi:10.1016/j.knosys.2011.11.011

Cited by 42 publications

(23 citation statements)

References 17 publications

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“…Noncooperative actions might take place among agents that serve the interests of truly distinct parties. Currently, noncooperative coordination has attracted much attention in multiagent systems where the agents are self-motivated and attempt to maximize their own benefits [23], [46]. Similarly, in social networks, there can be selfish actors; networks with such actors are called noncooperative social networks [88].…”

Section: Noncooperative Social Networkmentioning

confidence: 99%

Understanding Social Networks From a Multiagent Perspective

Jiang¹,

Jiang

2014

IEEE Trans. Parallel Distrib. Syst.

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Social networks have recently been widely explored in many fields; these networks are composed of a set of autonomous social actors and the interaction relations among them. Multiagent computing has already been widely envisioned to be a powerful paradigm for modeling autonomous multientity systems; therefore, it is promising to connect the research on social networks and multiagent systems. In general, there are three views for research on social networks: the structure-oriented view, in which only the network structure characteristics among actors are considered, the actor-oriented view, in which only the behavior characteristics of actors are considered, and the actor-structure crossing view, in which both actors and network structures are considered and their crossing effects are explored. This survey paper mainly concerns studies on social networks that have the last two views and discusses the relationship between social networks and multiagent systems. Because coordination is critical for both multiagent systems and social networks, this paper classifies studies on social networks that are based on the coordination mechanisms among the actors in the social networks. By referring to typical types of coordination situations in multiagent systems, social networks in previous studies can be classified into three classes: cooperative social networks, noncooperative social networks, and multiple social networks; for each class, this paper reviews the existing studies and discusses the challenge issues and possible future research directions. From this survey, we find that social networks can be understood well via a multiagent coordination perspective and also that many multiagent coordination techniques can be cogently applied in research on social networks. Moreover, this paper discusses the advantages and disadvantages of the multiagent coordination perspective by comparing with other perspectives on studying social networks.

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Section: Noncooperative Social Networkmentioning

confidence: 99%

Understanding Social Networks From a Multiagent Perspective

Jiang¹,

Jiang

2014

IEEE Trans. Parallel Distrib. Syst.

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“…Rosacis model [5] is based on agents reputation (REP) and reliability (REL). REL is the agent recommendation reliability which basically evaluates how trusted recommendations provided by an agent are.…”

Section: Rosaci Trust Modelmentioning

confidence: 99%

A framework for comparison of trust models for multi agent systems

Shehada

Zemerly

Yeun

et al. 2015

2015 International Conference on Information and Communication Technology Research (ICTRC)

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Agents technology plays an important role in the development of many major service applications. However, balancing between the flexible features agents provide, and their vulnerability to many security oriented attacks are considered a great challenge. In this paper we review trust models that are proposed in the literature to provide trustworthiness and security to Multi Agent Systems (MAS). We subsequently develop a framework for comparison of the various different trust models. Trust models are first compared and classified according to types of evaluations used, weight assignment, consideration of inaccurate evaluations and architecture. They are also compared according to suitability to MAS.

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“…For example, a user can exploit during his navigation an agent as a client which observes his behavior and in this way implicitly builds a model to represent his interests and preferences with respect to all the visited Web sites. For this purpose, agent-based systems exploit in their recommendation algorithms an internal representation (profile) of the user built by the associated software agent which monitors his Web activities [27,58,44,8,2,7,39,41]. If the user accesses a Web site, his agent can exploit the profile in the interaction with the site in order to provide both content-based and collaborative recommendations to the user agent by adapting the site presentation.…”

Section: A Promising Solution: Using Agent-based Recommender Systemsmentioning

confidence: 99%