NDLI: Multiagent AGVs dispatching system using multilevel decisions method

Content Provider	IEEE Xplore Digital Library
Author	Xiaomeng Li Tao Geng YuPu Yang Xiaoming Xu
Copyright Year	2002
Description	Author affiliation: Dept. of Autom., Shanghai Jiao Tong Univ., China (Xiaomeng Li; Tao Geng; YuPu Yang; Xiaoming Xu)
Abstract	An AGV dispatching system needs dynamic and distributed dispatching policies in FMS. This research addresses a multilevel decision and cooperative learning method to solve this problem. Each AGV is treated as a rational agent, which has two level decisions. On the option level an agent will make decisions to execute a sub-task with the best response to the other AGVs current option. On the action level, an agent will learn an optimal policy of actions for achieving his planned option. To implement the multi-level decision, we use Markov games and the reinforcement learning (RL) algorithm on the option level and a memory based algorithm RL on the action level. We apply our method to an AGV dispatching simulation and show the results.
Sponsorship	American Autom. Control Council IFAC SICE
Starting Page	1135
Ending Page	1136
File Size	174987
Page Count	2
File Format	PDF
ISBN	0780372980
ISSN	07431619
DOI	10.1109/ACC.2002.1023172
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Dispatching Vehicle dynamics Flexible manufacturing systems Throughput Automation System performance Assembly systems Learning Collaborative work Equations
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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