Certainty, trust and evidence: Towards an integrative model of confidence in multi-agent systems

Basheer, Ghusoon Salim; Ahmad, Mohd Sharifuddin; Tang, Alicia Y. C.; Graf, Sabine

doi:10.1016/j.chb.2014.12.030

Cited by 24 publications

(18 citation statements)

References 9 publications

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“…Learners can show a certain level of trust based on the quality of his contributions to the web forum (submitted by means of messages). In this case, we added to the previous model a corrective factor using the trust-based model described in [32]:…”

Section: Resultsmentioning

confidence: 99%

Adaptive e-Assessment System: A General Approach

Bañeres

Baró

Rodríguez

2016

Int. J. Emerg. Technol. Learn.

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Abstract-On any type of course (on site or online), a learner is evaluated whether he has acquired the knowledge and competences provided in the course. The evaluation should be performed by evaluating his progression by means of the interaction in the classroom or assessment activities. Mostly, assessment activities are used to check the level of expertise of the learner. Typically, the assessment model and assessment activities of subjects in official programmes are the same for all the learners, since they should be evaluated having the same opportunities and conditions. However, when the learner is evaluated based on a continuous assessment model, he is demonstrating on each activity his knowledge and proficiency level and, at the same time, his reputation could be also built based on the actions he is performing within the course. Therefore, the assessment model can be particularly adapted for each learned based on this information. In this paper, we present a general system to adapt any component of the assessment process (model, activity, question…) based on different evidences gathered from the learning process of the learner.

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Section: Resultsmentioning

confidence: 99%

Adaptive e-Assessment System: A General Approach

Bañeres

Baró

Rodríguez

2016

Int. J. Emerg. Technol. Learn.

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“…The proposed model [9] evaluates the level of confidence an agent has in another agent based on a local confidence (LC) and global confidence (GC) values calculated by an evaluating agent. Each confidence is calculated based on three values: the importance of the local or global confidences (I), the trust (T) and the certainty (C).…”

Section: Basheer Et Al 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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“…Multiagent systems (MASs) are computerized systems composed of multiple interacting intelligent agents that have limited energy and self-organization ability. To achieve a common goal, MASs require them to self-organize into a network and collaborate with one another [1]. MASs are used most often in the engineering and technology fields.…”

Section: Introductionmentioning

confidence: 99%

Packet Forwarding Strategies in Multiagent Systems: An Evolutionary Game Approach

2018

Wireless Communications and Mobile Computing

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In multiagent systems (MASs), agents need to forward packets to each other to accomplish a target task. In this paper, we study packet forwarding among agents using evolutionary game theory under the mechanisms of Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Packet forwarding among agents plays a key role to stabilize the whole MAS. We study the transfer probability of packet forwarding of agents at the idle state or the busy state and computer the probability of the packet forwarding for a MAS. When agents make their decisions to select Forward or No-Forward strategy, a packet forwarding evolutionary game model is built to reflect the utilities of different packet forwarding strategies. Two incentive mechanisms are introduced into the game model. One is to motivate agents to strengthen cooperation; the other is to encourage agents to select the No-Forward strategy to save energy while they are in the busy state. The parameter value that encourages an agent to select the No-Forward strategy is inversely proportional to the average probability of the packet forwarding. The replicator dynamics of agent packet forwarding strategy evolution are given. We propose and prove the theorems indicating that evolutionarily stable strategies (ESSs) can be attained. The results of simulation experiments verify the correctness of the proposed theorems and the effects of the two incentive mechanisms and the probability of packet forwarding, which assures the robustness of evolutionary stable points among agents in MASs.

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Certainty, trust and evidence: Towards an integrative model of confidence in multi-agent systems

Cited by 24 publications

References 9 publications

Adaptive e-Assessment System: A General Approach

Adaptive e-Assessment System: A General Approach

A framework for comparison of trust models for multi agent systems

Packet Forwarding Strategies in Multiagent Systems: An Evolutionary Game Approach

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