Using Reinforcement Learning for Agent-based Network Fault Diagnosis System
In the network, it is important that faults can be diagnosed at early stage before they result in serious fault. However, the situation is not optimistic, which depends on what network management software is used. Aiming to this problem, a mobile agent-based network fault diagnosis model is proposed. In the model, agent can learn by reinforcement learning (RL), which can improve fault diagnosis performance. The structure and function of model, especially the architecture and learning algorithm of diagnostic agent, is depicted. At last, compared the system performance through simulation and experiment, and results show that the model has greater advantage.
Jingang Cao
Department of Computer North Chino Electric Power University oodles, Hebei Province, China
国际会议
深圳
英文
750-754
2011-06-06（万方平台首次上网日期，不代表论文的发表时间）