Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehensive trust values based on multiple pieces of evidence from variant sources. The proposed trust model leverages information fusion and RL to properly fuse multiple pieces of evidence to generate trust value for every agent in MAT. The trust value is then used in an interaction protocol with MAT to increase the efficiency of cooperation. To verify the performance of the proposed trust model, a ball-collection experiment is designed for MAT to work cooperatively in simulation environments. Two different scenario settings are used to demonstrate the adaptability and robustness of the proposed trust model. The results are further compared with human-designed fusion methods. The comparison shows that the proposed trust model has a better representation of agent performance, namely convergence speed, than human-designed methods in different scenario settings.

Automated summary

This article proposes an adaptive trust model to evaluate comprehensive trust values based on multiple pieces of evidence, using information fusion and reinforcement learning to increase the efficiency of cooperation in multiagent teaming. The performance of the trust model is verified through experiments and compared with human-designed fusion methods, showing better representation of agent performance in different scenario settings.