Adaptive agent selection in large-scale multi-agent systems

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

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

1 Citation (Scopus)


An agent in a multi-agent system (MAS) has to select appropriate agents to assign tasks. Unfortunately no agent in an open environment can identify the states of all agents, so this selection must be done according to local information about the other known agents; however this information is limited and may contain uncertainty. In this paper we investigate how overall performance of MAS is affected by learning parameters for adaptive strategies to select partner agent for collaboration. We show experimental results using simulation and discuss why overall performance of MAS varies.

Original languageEnglish
Title of host publicationPRICAI 2006
Subtitle of host publicationTrends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Number of pages5
ISBN (Print)3540366679, 9783540366676
Publication statusPublished - 2006
Externally publishedYes
Event9th Pacific Rim International Conference on Artificial Intelligence - Guilin, China
Duration: 2006 Aug 72006 Aug 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4099 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th Pacific Rim International Conference on Artificial Intelligence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Adaptive agent selection in large-scale multi-agent systems'. Together they form a unique fingerprint.

Cite this