Learning Task Allocation for Multiple Flows in Multi-agent Systems

摘要：

Task allocation is a key problem for agent to reach cooperation in multi-agent systems. Lately task flows are replacing traditional static tasks, thus realtime dynamic task allocation mechanisms draw more attention. Though scheduling single task flow is well investigated, little work on allocation of multiple task flows has been done. In this paper a distributed and self-adaptable scheduling algorithm based on Qlearning for multiple task flows is proposed. This algorithm can not only adapt to task arrival process on itself, but also fully consider the influence from task flows on other agents. Besides, its distributed property guaranteed that it can be applied to open multi-agent systems with local view. Reinforcement learning makes allocation adapt to task load and node distribution. It is verified that this algorithM improves task throughput, and decreases average execution time per task.

关键词： Agent cooperation Task allocation Multi-agent system Multiple task flows Q-learning

作者: Zheng Xiao Shengxiang Ma Shiyong Zhang

作者单位: School of Computer Science Fudan University Shanghai, China School of Computer Science Fudan University Shanghai,China

会议类型: 国际会议

会议名称: The International Conference on Communication Software and Networks(2009 IEEE通信软件与网络国际会议 ICCSN 2009)

会议地点: 成都

会议语种:英文

页码: 153-157

在线出版日期: 2009-02-20（万方平台首次上网日期，不代表论文的发表时间）