WEKO3
アイテム
Learning of Task Allocation Method Based on Reorganization of Agent Networks in Known and Unknown Environments
https://ipsj.ixsq.nii.ac.jp/records/100832
https://ipsj.ixsq.nii.ac.jp/records/100832eb8116d6-6b77-4ffe-b8d5-5adeb4f935e6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL5504015 (778.2 kB)
|
Copyright (c) 2014 by the Information Processing Society of Japan
|
|
| オープンアクセス | ||
| Item type | Journal(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2014-04-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Learning of Task Allocation Method Based on Reorganization of Agent Networks in Known and Unknown Environments | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Learning of Task Allocation Method Based on Reorganization of Agent Networks in Known and Unknown Environments | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [特集：Multiagent-based Societal Systems] distributed task allocation, team formation, coordination, learning, reorganization | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Department of Computer Science and Engineering, Graduate School of Waseda University | ||||||||||
| 著者所属 | ||||||||||
| Department of Computer Science and Engineering, Graduate School of Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Computer Science and Engineering, Graduate School of Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Computer Science and Engineering, Graduate School of Waseda University | ||||||||||
| 著者名 |
Kazuki, Urakawa
× Kazuki, Urakawa
× Toshiharu, Sugawara
|
|||||||||
| 著者名(英) |
Kazuki, Urakawa
× Kazuki, Urakawa
× Toshiharu, Sugawara
|
|||||||||
| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | We propose a team formation method that integrates the estimating of the resources of neighboring agents in a tree-structured agent network in order to allocate tasks to the agents that have sufficient capabilities for doing tasks. A task for providing the required service in a distributed environment is often achieved by a number of subtasks that are dynamically constructed on demand in a bottom-up manner and then done by the team of appropriate agents. A number of studies were conducted on efficient team formation for quality services. However, most of them assume that resources in other agents are known, and this assumption is not adequate in real world applications. The contribution of this paper is threefold. First, we extend the conventional method by combining the learning of task allocation and the reorganization of agent networks. In particular, we introduce the elimination of links as well as the generation of links in the reorganization. Second, we revise the learning method so as to use only information available locally. Finally, we omitt the assumption that all resource information in other agents is given in advance. Instead, we extend the task allocation method by combining it with the resource estimation of neighboring agents. We experimentally show that this extension can considerably improve the efficiency of team formation compared with the conventional method even though it does not require knowledge of resources in other agents. We also show that it can make the agent network adaptive to environmental changes. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online) DOI http://dx.doi.org/10.2197/ipsjjip.22.289 ------------------------------ |
|||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | We propose a team formation method that integrates the estimating of the resources of neighboring agents in a tree-structured agent network in order to allocate tasks to the agents that have sufficient capabilities for doing tasks. A task for providing the required service in a distributed environment is often achieved by a number of subtasks that are dynamically constructed on demand in a bottom-up manner and then done by the team of appropriate agents. A number of studies were conducted on efficient team formation for quality services. However, most of them assume that resources in other agents are known, and this assumption is not adequate in real world applications. The contribution of this paper is threefold. First, we extend the conventional method by combining the learning of task allocation and the reorganization of agent networks. In particular, we introduce the elimination of links as well as the generation of links in the reorganization. Second, we revise the learning method so as to use only information available locally. Finally, we omitt the assumption that all resource information in other agents is given in advance. Instead, we extend the task allocation method by combining it with the resource estimation of neighboring agents. We experimentally show that this extension can considerably improve the efficiency of team formation compared with the conventional method even though it does not require knowledge of resources in other agents. We also show that it can make the agent network adaptive to environmental changes. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online) DOI http://dx.doi.org/10.2197/ipsjjip.22.289 ------------------------------ |
|||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 55, 号 4, 発行日 2014-04-15 |
|||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
