Role and member selection in team formation using resource estimation for large-scale multi-agent systems

Masashi Hayano*, Dai Hamada, Toshiharu Sugawara

*この研究の対応する著者

研究成果: Article査読

21 被引用数 (Scopus)

抄録

We propose an efficient team formation method for multi-agent systems consisting of self-interested agents in task-oriented domains where agents have no prior knowledge of the resources/abilities of the other agents. Internet services based on services computing and cloud computing, which have been rapidly increasing, are usually achieved by combining a number of service elements that are distributed over the Internet. We modelled the executions of these elements as teams of agents with the resources and abilities required in the corresponding service elements. This team formation method with the appropriate agents for the service elements makes the entire system efficient. Our proposed method is based on our previous parameter learning method that enables agents to identify their roles in forming a team but requires prior knowledge of all others' resources. This restricts the applicability to real systems. The contribution of this paper is twofold. First, we extended our original method by adding a resource estimation method. Second, we further improved the first extension for large scale multi-agent systems by introducing purviews, which are a relatively small set of agents that are potential members of the teams, for practical computational time and required memory size. We experimentally evaluated our first method by comparing it with the previous method and the task allocation using the contract net protocol (CNP). Then, after increasing the number of agents, we evaluated our second extended method and investigated how the number of agents and the size of the purview affected the overall performances. Results showed that the learning speed was faster in the proposed method so it outperformed other methods in a practical sense even though it did not require prior knowledge of resources in other agents in busy, large-scale, multi-agent systems.

本文言語English
ページ(範囲)164-172
ページ数9
ジャーナルNeurocomputing
146
DOI
出版ステータスPublished - 2014 12 25

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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