Adaptive switching behavioral strategies for effective team formation in changing environments

Masashi Hayano*, Yuki Miyashita, Toshiharu Sugawara

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This paper proposes a control method for in agents by switching their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. Advances in computer science, telecommunications, and electronic devices have led to proposals of a variety of services on the Internet that are achieved by teams of different agents. To provide these services efficiently, the tasks to achieve them must be allocated to appropriate agents that have the required capabilities, and the agents must not be overloaded. Furthermore, agents have to adapt to dynamic environments, especially to frequent changes in workload. Conventional decentralized allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in the entire system becoming inefficient due to the concentration of task allocation when the workload becomes high. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability to avoid conflicts in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluated our method in static and dynamic environments where the number of tasks varied.

Original languageEnglish
Title of host publicationAgents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers
EditorsJoaquim Filipe, Jaap van den Herik
PublisherSpringer Verlag
Pages37-55
Number of pages19
ISBN (Print)9783319533537
DOIs
Publication statusPublished - 2017
Event8th International Conference on Agents and Artificial Intelligence, ICAART 2016 - Rome, Italy
Duration: 2016 Feb 242016 Feb 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10162 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Agents and Artificial Intelligence, ICAART 2016
Country/TerritoryItaly
CityRome
Period16/2/2416/2/26

Keywords

  • Agent network
  • Allocation problem
  • Bottom-up organization
  • Reciprocity
  • Team formation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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