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  1. 研究論文

A Quantified Distributed Constraint Optimization Problem

https://nitech.repo.nii.ac.jp/records/3454
https://nitech.repo.nii.ac.jp/records/3454
046a5d68-b0f8-4884-8cb8-567cb2a123c5
名前 / ファイル ライセンス アクション
matsui_2010_aamas_1.pdf 本文_fulltext (277.9 kB)
Richland, SC (c) 2010
Item type 会議発表論文 / Conference Paper(1)
公開日 2013-06-25
タイトル
タイトル A Quantified Distributed Constraint Optimization Problem
言語 en
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者 Matsui, Toshihiro

× Matsui, Toshihiro

en Matsui, Toshihiro

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Matsuo, Hiroshi

× Matsuo, Hiroshi

en Matsuo, Hiroshi

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Silaghi, Marius Calin

× Silaghi, Marius Calin

en Silaghi, Marius Calin

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Hirayama, Katsutoshi

× Hirayama, Katsutoshi

en Hirayama, Katsutoshi

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Yokoo, Makoto

× Yokoo, Makoto

en Yokoo, Makoto

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Baba, Satomi

× Baba, Satomi

en Baba, Satomi

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著者別名
識別子Scheme WEKO
識別子 8674
識別子Scheme NRID
識別子URI http://rns.nii.ac.jp/nr/1000060437093
識別子 1000060437093
姓名 松井, 俊浩
著者別名
識別子Scheme WEKO
識別子 8515
識別子Scheme NRID
識別子URI http://rns.nii.ac.jp/nr/1000000219396
識別子 1000000219396
姓名 松尾, 啓志
書誌情報 en : Proc. 9th Int'l. Conf. on Autonomous Agents and Multiagent Systems (AAMAS2010)

巻 1, p. 1023-1030, 発行日 2010-05
出版者
出版者 The International Foundation for Autonomous Agents and Multiagent Systems (IDFAAMS)
言語 en
著者版フラグ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
内容記述
内容記述タイプ Other
内容記述 In this paper, we propose a Quantifie Distributed Constraint Optimization problem (QDCOP) that extends the framework of Distributed Constraint Optimization problems (DCOPs). DCOPs have been studied as a fundamental model of multi-agent cooperation. In traditional DCOPs, all agents cooperate to optimize the sum of their cost functions. However, in practical systems some agents may desire to select the value of their variables without cooperation. In special cases, such agents may take the values with the worst impact on the quality of the result reachable by the optimization process. We apply existential/universal quantifier to distinct uncooperative variables. A universally quantifie variable is left unassigned by the optimization as the result has to hold when it takes any value from its domain, while an existentially quantifie variable takes exactly one of its values for each context. Similar classes of problems have recently been studied as (Distributed) Quantifie Constraint Problems, where the variables of the CSP have quantifiers All constraints should be satisfie independently of the value taken by universal variables. We propose a QDCOP that applies the concept of game tree search to DCOP. If the original problem is a minimization problem, agents that own universally quantifie variables may intend to maximize the cost value in the worst case. Other agents normally intend to optimize the minimizing problems. Therefore, only the bounds, especially the upper bounds, of the optimal value are guaranteed. The purpose of the new class of problems is to compute such bounds, as well as to compute sub-optimal solutions. For the QDCOP, we also propose several methods that are based on min-max/alpha-beta and ADOPT algorithms.
言語 en
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