Presentation | 2009-09-25 Improvement of team formations according to organizational structures and reorganization Ryota KATAYANAGI, Toshiharu SUGAWARA, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Multi-Agent System(MAS) / Q-learning / Team Formation / Reorganization |
Paper # | AI2009-17 |
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Committee | AI |
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Conference Date | 2009/9/18(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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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 |
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