Adaptive probabilistic task allocation in large-scale multi-agent systems and its evaluation

Toshiharu Sugawara, Kensuke Fukuda, Toshio Hirotsu, Satoshi Kurihara

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

Abstract

In this paper, we introduce the probabilistic awardee selection strategy, under which awardee is selected with a fixed probability, into the award phase of contract net protocol. We then point out that, by changing the probabilities in this strategy according the local workload, the overall performance can be considerably improved.

Original languageEnglish
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
Pages1311-1312
Number of pages2
DOIs
Publication statusPublished - 2010 Aug 27
Externally publishedYes
Event12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States
Duration: 2010 Jul 72010 Jul 11

Publication series

NameProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Other

Other12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
CountryUnited States
CityPortland, OR
Period10/7/710/7/11

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Keywords

  • Adaptive behavior
  • Contract net protocol
  • Distributed task allocation
  • Load-balancing
  • Optimization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Sugawara, T., Fukuda, K., Hirotsu, T., & Kurihara, S. (2010). Adaptive probabilistic task allocation in large-scale multi-agent systems and its evaluation. In Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 (pp. 1311-1312). (Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10). https://doi.org/10.1145/1830483.1830718