An efficient trust estimation model for multi-agent systems using temporal difference learning
| Autor: | S. G. Ponnambalam, G. Rishwaraj, Loo Chu Kiong |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Estimation
0209 industrial biotechnology Teamwork Knowledge management business.industry Computer science Multi-agent system media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre 020901 industrial engineering & automation Artificial Intelligence Order (exchange) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Temporal difference learning computer Software media_common |
| Zdroj: | Neural Computing and Applications. 28:461-474 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | In multi-agent system (MAS) applications, teamwork among the agents is essential as the agents are required to collaborate and pool resources to execute the given tasks and complete the objectives successfully. A vital part of the collaboration is sharing of information and resources in order to optimize their efforts in achieving the given objectives. Under such collaborative environment, trust among the agents plays a critical role to ensure efficient cooperation. This study looks into developing a trust evaluation model that can empirically evaluate the trust of one agent on the other. The proposed model is developed using temporal difference learning method, incorporating experience gained through interactions into trust evaluation. Simulation experiments are conducted to evaluate the performance of the developed model against some of the most recent models reported in the literature. The results of the simulation experiments indicate that the proposed model performs better than the comparison models in estimating trust more effectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink