مورد إلكتروني
Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion
| العنوان: | Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion |
|---|---|
| المؤلفون: | Lin, CT, Zhang, H, Ou, L, Chang, YC, Wang, YK |
| بيانات النشر: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2024-02-01 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | 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. |
| مصطلحات الفهرس: | Journal Article |
| URL: | IEEE Transactions on Emerging Topics in Computational Intelligence 10.1109/TETCI.2023.3319253 United States Department of the NavyN629091912058 |
| الإتاحة: | Open access content. Open access content info:eu-repo/semantics/embargoedAccess |
| أرقام أخرى: | LT1 oai:opus.lib.uts.edu.au:10453/176100 IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8, (1), pp. 229-239 2471-285X 2471-285X 1439678220 |
| المصدر المساهم: | UNIV OF TECH, SYDNEY From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1439678220 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |