Role and member selection in team formation using resource estimation

Masashi Hayano, Dai Hamano, Toshiharu Sugawara

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

2 Citations (Scopus)

Abstract

We propose an efficient team formation method for multi-agent systems consisting of self-interested agents in task-oriented domains. Services computing on computer networks have been rapidly increasing. Efficient team formation for service tasks is considered to be a way to improve performance. Our method is based on our previous parameter learning method enabling agents to efficiently form teams but requiring prior knowledge about all others' resources. We extended that method by adding a resource estimation method so as to increase its applicability to actual application systems. We experimentally evaluated our method by comparing it with the previous method and the task allocation using contract net protocol (CNP). The results demonstrated that the proposed method outperformed other methods even though it did not require prior knowledge about resources in other agents. We discuss the reason for this improvement.

Original languageEnglish
Title of host publicationAdvanced Methods and Technologies for Agent and Multi-Agent Systems
EditorsDariusz Barbucha, Le Manh Thanh, Robert Howlett, Lakhmi Jain
Pages125-136
Number of pages12
DOIs
Publication statusPublished - 2013 Dec 1

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume252
ISSN (Print)0922-6389

Keywords

  • Learning
  • Task Allocation
  • Team Formation

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Hayano, M., Hamano, D., & Sugawara, T. (2013). Role and member selection in team formation using resource estimation. In D. Barbucha, L. Manh Thanh, R. Howlett, & L. Jain (Eds.), Advanced Methods and Technologies for Agent and Multi-Agent Systems (pp. 125-136). (Frontiers in Artificial Intelligence and Applications; Vol. 252). https://doi.org/10.3233/978-1-61499-254-7-125