Aggregating local behaviors based upon a discrete Lagrange multiplier method

Yi Tang, Jiming Liu, Xiaolong Jin

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

When solving a distributed problem based on a multi-agent system, the local behaviors of agents are aggregated to the global behaviors of the multi-agent system towards a solution state. This work presents a distributed discrete Lagrange multiplier (DDLM) method for solving distributed constraint satisfaction problems (distributed CSPs). In this method, the local behaviors of agents are aggregated as a descent direction of an objective function corresponding to the problem at hand. Thus, a trend to a solution state are formed. Furthermore, we provide three techniques to speed up the aggregation of agents' local behaviors. Through experiments on benchmark graph coloring problems, we validate the effectiveness of the presented DDLM method as well as the three techniques in solving distributed CSPs.
Original languageEnglish
Title of host publicationProceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004. (IAT 2004)
PublisherIEEE
Pages413-416
Number of pages4
ISBN (Print)0769521010
DOIs
Publication statusPublished - 24 Sept 2004
EventIEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2004 - Beijing, China
Duration: 20 Sept 200424 Sept 2004
https://ieeexplore.ieee.org/xpl/conhome/9301/proceeding

Publication series

NameIEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT)

Conference

ConferenceIEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2004
Country/TerritoryChina
CityBeijing
Period20/09/0424/09/04
Internet address

User-Defined Keywords

  • Lagrangian functions
  • Multiagent systems
  • Character generation
  • Information science
  • Information security
  • Content addressable storage
  • Computer science
  • Quaternions
  • Chromium
  • Discrete transforms

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