Title :
Neural network-based reputation model in a distributed system
Author :
Song, Weihua ; Phoha, Vir V.
Author_Institution :
Dept. of Comput. Sci., Louisiana Tech Univ., Ruston, LA, USA
Abstract :
Current centralized trust models are inappropriate to apply in a large distributed multi-agent system, due to various evaluation models and partial observations in local level reputation management. This paper proposes a distributed reputation management structure, and develops a global reputation model. The global reputation model is a novel application of neural network techniques in distributed reputation evaluations. The experimental results showed that the model has robust performance under various estimation accuracy requirements. More important, the model is adaptive to changes in distributed system structures and in local reputation evaluations.
Keywords :
multi-agent systems; neural nets; centralized trust models; distributed multi-agent system; distributed reputation evaluations; distributed reputation management; distributed system; estimation accuracy requirements; evaluation models; global reputation model; local reputation evaluations; neural network; partial observations; robust performance; Aggregates; Computer science; Context modeling; Intelligent networks; Load management; Master-slave; Memory management; Multiagent systems; Neural networks; Robustness;
Conference_Titel :
e-Commerce Technology, 2004. CEC 2004. Proceedings. IEEE International Conference on
Print_ISBN :
0-7695-2098-7
DOI :
10.1109/ICECT.2004.1319751
