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

Masashi Hayano, Dai Hamada, Toshiharu Sugawara

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)164-172
Number of pages9
JournalNeurocomputing
Volume146
DOIs
Publication statusPublished - 2014 Dec 25

Keywords

  • Learning
  • Task allocation
  • Team formation

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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