Presentation 2009-09-25
Improvement of team formations according to organizational structures and reorganization
Ryota KATAYANAGI, Toshiharu SUGAWARA,
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Abstract(in English) 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 affect team formations of the agents. We will show that the utility and the failure of the team formation is deeply affected by depth of the tree structure.
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Keyword(in English) Multi-Agent System(MAS) / Q-learning / Team Formation / Reorganization
Paper # AI2009-17
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Conference Information
Committee AI
Conference Date 2009/9/18(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) Improvement of team formations according to organizational structures and reorganization
Sub Title (in English)
Keyword(1) Multi-Agent System(MAS)
Keyword(2) Q-learning
Keyword(3) Team Formation
Keyword(4) Reorganization
1st Author's Name Ryota KATAYANAGI
1st Author's Affiliation Waseda University()
2nd Author's Name Toshiharu SUGAWARA
2nd Author's Affiliation Waseda University
Date 2009-09-25
Paper # AI2009-17
Volume (vol) vol.109
Number (no) 211
Page pp.pp.-
#Pages 6
Date of Issue