A Probabilistic Approach for Maintaining Trust Based on Evidence

Social Network Group Decision-Making Method Based on Q-Rung Orthopair Fuzzy Set and Its Application in the Evaluation of Online Teaching Quality

As q-rung orthopair fuzzy set (q-ROFS) theory can effectively express complex fuzzy information, this study explores its application to social network environments and proposes a social network group decision-making (SNGDM) method based on the q-ROFS. Firstly, the q-rung orthopair fuzzy value is used to represent the trust relationships between experts in the social network, and a trust q-rung orthopair fuzzy value is defined. Secondly, considering the decreasing and multipath of trust in the process of trust propagation, this study designs a trust propagation mechanism by using its multiplication operation in the q-ROFS environment and proposes a trust q-ROFS aggregation approach. Moreover, based on the trust scores and confidence levels of experts, a new integration operator called q-rung orthopair fuzzy-induced ordered weighted average operator is proposed to fuse experts’ evaluation information. Additionally, considering the impact of consensus interaction on decision-making results, a consensus interaction model based on the q-ROF distance measure and trust relationship is proposed, including consistency measurement, identification of inconsistent expert decision-making opinions and a personalized adjustment mechanism. Finally, the SNGDM method is applied to solve the problem of evaluating online teaching quality.

Reliability Model Based Dynamic Multi-Level Trust Analysis

Trust assessment is of great significance to related issues such as privacy protection and rumor transmission in online social networks. At present, there are mainly two types of trust models: discrete type and continuous, type. Little work has been done on considering distrust and trust at the same time, especially on the propagation mechanism of distrust. To this end, this paper proposes a multilevel trust model and a corresponding trust evaluation method based on a reliability model called multivalued decision diagrams (MDDs). The proposed trust model combines characteristics of both discrete and continuous trust and considers the dynamic changing mechanism of the multilevel trust. The propagation of distrust and conflicts of opinions are also handled. Experimental results show that the proposed method outperforms other existing methods.

Heuristics-Based Trust Estimation in Multiagent Systems Using Temporal Difference Learning

The application of multiagent system (MAS) is becoming increasing popular as it allows agents in a system to pool resources together to achieve a common objective. A vital part of the MAS is the teamwork cooperation through the sharing of information and resources among the agents to optimize their efforts in accomplishing given objectives. A critical part of the teamwork effort is the ability to trust each other when executing any task to ensure efficient and successful cooperation. This paper presents the development of a trust estimation model that could empirically evaluate the trust of an agent in MAS. The proposed model is developed using temporal difference learning by incorporating the concept of Markov games and heuristics to estimate trust. Simulation experiments are conducted to test and evaluate the performance of the developed model against some of the recently reported model in the literature. The simulation experiments indicate that the developed model performs better in terms of accuracy and efficiency in estimating trust.