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

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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 publicationStudies in Computational Intelligence
Pages217-230
Number of pages14
Volume56
DOIs
Publication statusPublished - 2007
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume56
ISSN (Print)1860949X

Fingerprint

Multi agent systems
Intelligent agents
Program processors
Internet
Bandwidth
Communication

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Sugawara, T., Fukuda, K., Hirotsu, T., Sato, S. Y., & Kurihara, S. (2007). Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection. In Studies in Computational Intelligence (Vol. 56, pp. 217-230). (Studies in Computational Intelligence; Vol. 56). https://doi.org/10.1007/978-3-540-71075-2_17

Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection. / Sugawara, Toshiharu; Fukuda, Kensuke; Hirotsu, Toshio; Sato, Shin Ya; Kurihara, Satoshi.

Studies in Computational Intelligence. Vol. 56 2007. p. 217-230 (Studies in Computational Intelligence; Vol. 56).

Research output: Chapter in Book/Report/Conference proceedingChapter

Sugawara, T, Fukuda, K, Hirotsu, T, Sato, SY & Kurihara, S 2007, Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection. in Studies in Computational Intelligence. vol. 56, Studies in Computational Intelligence, vol. 56, pp. 217-230. https://doi.org/10.1007/978-3-540-71075-2_17
Sugawara T, Fukuda K, Hirotsu T, Sato SY, Kurihara S. Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection. In Studies in Computational Intelligence. Vol. 56. 2007. p. 217-230. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-540-71075-2_17
Sugawara, Toshiharu ; Fukuda, Kensuke ; Hirotsu, Toshio ; Sato, Shin Ya ; Kurihara, Satoshi. / Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection. Studies in Computational Intelligence. Vol. 56 2007. pp. 217-230 (Studies in Computational Intelligence).
@inbook{d092424b02284e76abd036cc5c432b2c,
title = "Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection",
abstract = "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.",
author = "Toshiharu Sugawara and Kensuke Fukuda and Toshio Hirotsu and Sato, {Shin Ya} and Satoshi Kurihara",
year = "2007",
doi = "10.1007/978-3-540-71075-2_17",
language = "English",
isbn = "3540710736",
volume = "56",
series = "Studies in Computational Intelligence",
pages = "217--230",
booktitle = "Studies in Computational Intelligence",

}

TY - CHAP

T1 - Improvements in performance of large-scale multi-agent systems based on the adaptive/non-adaptive agent selection

AU - Sugawara, Toshiharu

AU - Fukuda, Kensuke

AU - Hirotsu, Toshio

AU - Sato, Shin Ya

AU - Kurihara, Satoshi

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=34247580264&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34247580264&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-71075-2_17

DO - 10.1007/978-3-540-71075-2_17

M3 - Chapter

AN - SCOPUS:34247580264

SN - 3540710736

SN - 9783540710738

VL - 56

T3 - Studies in Computational Intelligence

SP - 217

EP - 230

BT - Studies in Computational Intelligence

ER -