Efficiency of emergent constraint satisfaction in small-world and random agent networks

Xiaolong Jin*, Jiming Liu

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

In this paper, we study the efficiency of emergent constraint satisfaction in small-world and random agent networks. We find that emergent constraint satisfaction in a small-world network is less efficient than in some other networks (e.g., regular networks). Further, we find that this finding holds in almost all random networks. Based on these observations, we study the relationship between the efficiency of emergent constraint satisfaction and the randomness of the corresponding network from which the constraint satisfaction problem is generated.

Original languageEnglish
Title of host publicationProceedings - IEEE/WIC International Conference on Intelligent Agent Technology, IAT'03
Pages304-310
Number of pages7
Publication statusPublished - 2003
Event2003 IEEE/WIC International Joint Conference on Intelligent Agent Technology and Web Intelligence, IAT'03 and WI'03 - Halifax, NS, Canada
Duration: 13 Oct 200317 Oct 2003

Publication series

NameProceedings - IEEE/WIC International Conference on Intelligent Agent Technology, IAT'03

Conference

Conference2003 IEEE/WIC International Joint Conference on Intelligent Agent Technology and Web Intelligence, IAT'03 and WI'03
Country/TerritoryCanada
CityHalifax, NS
Period13/10/0317/10/03

Scopus Subject Areas

  • Artificial Intelligence

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