Multi-agent systems are increasingly popular for modeling distributed environments that are highly complex and dynamic such as e-commerce, smart buildings, and smart grids. Often in open multi-agent systems, agents interact with other agents to meet their own goals. Trust is considered significant in multi-agent systems to make interactions effectively, especially when agents cannot assure that potential partners share the same core beliefs about the system or make accurate statements regarding their competencies and abilities. This work describes a trust model that augments fuzzy logic with Q-learning, and a suspension technique to help trust evaluating agents select beneficial trustees for interaction in uncertain, imprecise, and the dynamic multi-agent systems. Q-Learning is used to evaluate trust on the long term, fuzzy inferences are used to aggregate different trust factors and suspension is used as a short-term response to dynamic changes. The performance of the proposed model is evaluated using simulation. Simulation results indicate that the proposed model can help agents select trustworthy partners to interact with. It has a better performance compared to some of the popular trust models in the presence of misbehaving interaction partners.
When interactions are based on trust, trust establishment mechanisms can be used to direct trustees, instead of trustors, to build a higher level of trust and have a greater impact on the results of interactions. This work also describes a trust establishment model for intelligent agents using implicit feedback that goes beyond trust evaluation to outline actions to guide trustees (instead of trustors). The model uses the retention of trustors to model trustors’ behaviours. For situations where tasks are multi-criteria and explicit feedback is available, we present a trust establishment model that uses a multi-criteria approach to help trustees to adjust their behaviours to improve their perceived trust and attract more interactions with trustors. The model calculates the necessary improvement per criterion when only a single aggregated satisfaction value is provided per interaction, where the model attempts to predicted both the appropriate value per criteria and its importance. Then we present a trust establishment model that integrates the two major sources of information to produce a comprehensive assessment of a trustor’s likely needs in multi-agent systems. Specifically, the model attempts to incorporates explicit feedback, and implicit feed-back assuming multi-criteria tasks. The proposed models are evaluated through simulation, we found that trustees can enhance their trustworthiness, at a cost, if they tune their behaviour in response to feedback (explicit or implicit) from trustors. Using explicit feedback with multi-criteria tasks, trustees can emphasize on important criterion to satisfy need of trustors. Trust establishment based on explicit feedback for multi-criteria tasks, can result in a more effective and efficient trust establishment compared to using implicit feedback alone. Integrating both approaches together can achieve a reasonable trust level at a relatively lower cost.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/37617 |
| Date | 09 May 2018 |
| Creators | Aref, Abdullah |
| Contributors | Tran, Thomas |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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