Proposal of a method of effective teamformation using dynamic reorganization and its evaluation

Ryota Katayanagi*, Toshiharu Sugawara

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

We propose an effective method of dynamic reorganization using reinforcement learning for the team formation in multi-agent systems (MAS). A task in MAS usually consists of a number of subtasks that require their own resources, and it has to be processed in the appropriate team whose agents have the sufficient resources. The resources required for tasks are often unknown a priori and it is also unknown whether their organization is appropriate to form teams for the given tasks or not. Therefore, their organization should be adopted according to the environment where agents are deployed. In this paper, we investigated how the structures of network and the number of tasks affect team formations of the agents. We will show that the utility and the success of the team formation is deeply affected by depth of the tree structure and number of tasks.

本文言語English
ページ(範囲)76-85
ページ数10
ジャーナルTransactions of the Japanese Society for Artificial Intelligence
26
1
DOI
出版ステータスPublished - 2011
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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