Heuristics-Based Trust Estimation in Multiagent Systems Using Temporal Difference Learning
提出一种结合时间差分学习和启发式方法的信任估计模型，用于多智能体系统中评估智能体间的信任，仿真实验表明该模型在准确性和效率上优于现有模型。
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.