NDLI: Learning Task Allocation for Multiple Flows in Multi-Agent Systems

Content Provider	IEEE Xplore Digital Library
Author	Zheng Xiao Shengxiang Ma Shiyong Zhang
Copyright Year	2009
Abstract	Task allocation is a key problem for agent to reach cooperation in multi-agent systems. Lately task flows are replacing traditional static tasks, thus real-time 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 Q-learning 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.
Starting Page	153
Ending Page	157
File Size	924322
Page Count	5
File Format	PDF
ISBN	9780769535227
DOI	10.1109/ICCSN.2009.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiagent systems Q-learning Multi-agent system Agent cooperation Task allocation Throughput Dynamic scheduling Electronic mail Scheduling algorithm Learning Computer science Multiple task flows Communication system software Processor scheduling Collaboration
Content Type	Text
Resource Type	Article

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