Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection

Toshiharu Sugawara*, Kensuke Fukuda, Toshio Hirotsu, Shin Ya Sato, Satoshi Kurihara

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


An intelligent agent in a multi-agent system (MAS) often has to select appropriate agents to assign tasks that cannot be executed locally. These collaborating agents are usually determined based on their skills, abilities, and specialties. However, a more efficient agent is preferable if multiple candidate agents still remain This efficiency is affected by agents' workloads and CPU performance as well as the available communication bandwidth. Unfortunately, as no agent in an open environment such as the Internet can obtain these data from any of the other agents, this selection must be done according to the available local information about the other known agents. However, this information is limited and usually uncertain. Agents' states may also change over time, so the selection strategy must be adaptive to some extent. We investigated how the overall performance of MAS would change under adaptive strategies. We particularly focused on mutual interference by selection in different workloads, that is, underloaded, near-critial and overloaded stituations. This paper presents the simulation results and shows the overall performance of MAS highly depends on the workloads. Then we explain how adaptive strategies degrade overall performance when agents' workloads are near the limit of theoretical total capabilities.

Original languageEnglish
Title of host publicationEmergend Intelligence of Networked Agents
EditorsAkira Namatame, Hideyuki Nakashima, Satoshi Kurihara
Number of pages14
Publication statusPublished - 2007
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection'. Together they form a unique fingerprint.

Cite this