A Biologically Inspired Computational Trust Model based on the Perspective of the Trustee

Lygizou, Zoi; Kalles, Dimitris

doi:10.1145/3549737.3549748

Cited by 2 publications

(6 citation statements)

References 11 publications

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“…The majority of trust modeling research to date has focused on the evaluation of this expectation by the trustor agent, in order to estimate the trustworthiness of each potential partner (trustee) and thus to decide whether a trustee is reliable enough to interact with. We refer to this approach as the TCT approach [14]. Here, we outline its two key characteristics:…”

Section: The Notion Of Trustmentioning

confidence: 99%

“…CA is a computational trust model inspired by biology concepts, such as the synaptic plasticity of human brain and the formation of assemblies of neurons. In our previous work [14], we describe synaptic plasticity and how this biological process is transferred in our model, in detail.…”

Section: Ca Modelmentioning

confidence: 99%

“…We use a MAS to represent a social environment of agents who communicate with each other to execute tasks. In [14] we have formally defined all terms and concepts, necessary for the description of our model. Here, we only describe a few key concepts.…”

Section: Ca Modelmentioning

confidence: 99%

“…To deal with this drawback of TCT trust and reputation systems, we have already [14] introduced Create Assemblies (CA) trust model, a new approach in computational trust modeling from the perspective of the trustee, inspired by aspects of the synaptic plasticity of the brain. According to our approach, the trustor does not have to choose one of several available trustees, but it is the trustee who decides whether it has the skills to perform the task or not.…”

Section: Introductionmentioning

confidence: 99%

“…In [14] we provided an introductory experimental validation of the fundamental premises of our model. Our initial experiments indicated our model's resilience to highly dynamic and open systems.…”

Section: Introductionmentioning

confidence: 99%

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A biologically inspired computational trust model for open multi-agent systems which is resilient to unwillingness and dishonesty in reporting of trust information

Lygizou

Kalles

2023

Preprint

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Selfish agents in highly dynamic and open multi-agent systems are usually unwilling to exchange information with each another and usually dishonest when doing so. Thus, the use of trust information provided by other agents makes most trust and reputation systems susceptible to various sorts of disinformation. To deal with this issue, we have proposed a computational trust model from the perspective of the trustee, inspired by aspects of the synaptic plasticity of the brain, in which the trustor does not choose a trustee to delegate a task, but it is the trustee who decides whether it has the skills to perform the task or not. The proposed trust model follows the decentralized approach, it is developed so as to deal with the requirements of highly dynamic and open multi-agent systems, and since agents do not exchange trust information, it is immune to various sorts of disinformation. We conducted an extensive empirical evaluation of our model comparing it with FIRE, an established trust and reputation model for open multi-agent systems, and observed that our model was able to perform consistently in various dynamic conditions, maintaining a high level of utility gain for the agents and being immune to unwillingness and dishonesty in reporting of trust information.

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Section: The Notion Of Trustmentioning

confidence: 99%

Section: Ca Modelmentioning

confidence: 99%

Section: Ca Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A biologically inspired computational trust model for open multi-agent systems which is resilient to unwillingness and dishonesty in reporting of trust information

Lygizou

Kalles

2023

Preprint

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Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

Lygizou,

Kalles

2024

MAKE

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Recent work on decentralized computational trust models for open multi-agent systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee’s perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents’ continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor, and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q-Learning (DQL), in a single-agent reinforcement learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.

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A Biologically Inspired Computational Trust Model based on the Perspective of the Trustee

Cited by 2 publications

References 11 publications

A biologically inspired computational trust model for open multi-agent systems which is resilient to unwillingness and dishonesty in reporting of trust information

A biologically inspired computational trust model for open multi-agent systems which is resilient to unwillingness and dishonesty in reporting of trust information

Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

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