Local interaction of agents for division of labor in multi-agent systems

Wonki Lee, Dae Eun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Task allocation problem has been an issue in multi-agent systems. Among many interesting tasks, we focus on an algorithm for the proportional regulation of population where the swarm is divided into groups depending on task demands. We take the response threshold model inspired by division of labor in several social insects. In our approach, the member proportion of each sub-group is regulated proportional to the external task demands and local social interactions among agents. Here, the interactions control the response thresholds for given tasks. The proposed algorithm was applied to simulation experiments of robots, and the experimental results show that the proposed method has adaptive and robust responses under dynamically changing environments.

Original languageEnglish
Title of host publicationFrom Animals to Animats - 14th International Conference on Simulation of Adaptive Behavior, SAB 2016, Proceedings
EditorsJohn Hallam, Elio Tuci, Alexandros Giagkos, Myra Wilson
PublisherSpringer Verlag
Pages46-54
Number of pages9
ISBN (Print)9783319434872
DOIs
Publication statusPublished - 2016
Event14th International Conference on Simulation of Adaptive Behavior, SAB 2016 - Aberystwyth, United Kingdom
Duration: 2016 Aug 232016 Aug 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9825 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Simulation of Adaptive Behavior, SAB 2016
Country/TerritoryUnited Kingdom
CityAberystwyth
Period16/8/2316/8/26

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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