Presentation 2013-11-28
Efficient team formation in a large-scale environment
Masashi HAYANO, Toshiharu SUGAWARA,
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Abstract(in English) We propose an efficient team formation method for multi-agent system in a large-scale environment. We previously proposed the parameter learning method that enables agents to identify their roles through team formation without assumption that agents have information about resources of other agents. However, this method assumes that each agent have to maintain the learning parameters for all other agents. Thus, it required consider- able computational time and large memory when the number of agents increased. To overcome this problem, we introduce the concept "purviews," which are the small set of agents that are the potential members of the future teams. They also used to restrict the agents to learn their resources and their parameters for role decision, their parameters for role Agents also revise the purviews according to the contribution in the team formation. The result shows that the proposed method increase the received utilities in comparison with our previous method in large-scale, busy multi-agent systems.
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Keyword(in English) team formation / task allocation
Paper # AI2013-20
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Conference Information
Committee AI
Conference Date 2013/11/21(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Efficient team formation in a large-scale environment
Sub Title (in English)
Keyword(1) team formation
Keyword(2) task allocation
1st Author's Name Masashi HAYANO
1st Author's Affiliation Waseda Univercity()
2nd Author's Name Toshiharu SUGAWARA
2nd Author's Affiliation Waseda Univercity
Date 2013-11-28
Paper # AI2013-20
Volume (vol) vol.113
Number (no) 332
Page pp.pp.-
#Pages 6
Date of Issue