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Effective Acceptance Strategy Using Deep Reinforcement Learning in Bilateral Multi-issue Negotiation
https://ipsj.ixsq.nii.ac.jp/records/231838
https://ipsj.ixsq.nii.ac.jp/records/231838c6b02817-f7a5-4475-985e-15b1dc216a35
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-JNL6501006.pdf (662.3 kB)
2026年1月15日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, 論文誌:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2024-01-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Effective Acceptance Strategy Using Deep Reinforcement Learning in Bilateral Multi-issue Negotiation | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Effective Acceptance Strategy Using Deep Reinforcement Learning in Bilateral Multi-issue Negotiation | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [特集:エージェント理論・技術とその応用] acceptance strategy, automated negotiation, multi-agent systems, deep reinforcement learning | |||||||||
| 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 | ||||||||||
| Tokyo University of Agriculture and Technology | ||||||||||
| 著者所属 | ||||||||||
| The author is with Institute of Global Innovation Research, Tokyo University of Agriculture and Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Tokyo University of Agriculture and Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Institute of Global Innovation Research, Tokyo University of Agriculture and Technology | ||||||||||
| 著者名 |
Hyuga, Matsuo
× Hyuga, Matsuo
× Katsuhide, Fujita
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| 著者名(英) |
Hyuga, Matsuo
× Hyuga, Matsuo
× Katsuhide, Fujita
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Recently, automated negotiation has been attracting attention in multi-agent systems to resolve conflicts and reach an agreement among agents. In automated negotiation, two main types of strategies are incorporated in each agent: a bidding strategy that considers what kind of bid to send to an opponent, and an acceptance strategy that considers whether to accept the opponent's offer. In most bilateral multi-issue negotiation, agents take turns sending bids to each other and the negotiation ends when an agent accepts an opponent's offer. Therefore, the acceptance strategy is important in terms of increasing the utility of an agent. However, most studies of automated negotiation using reinforcement learning focus only on the bidding strategy of the agent, so there are not many studies that investigate acceptance strategies using reinforcement learning. In this paper, we propose a new configuration of a deep reinforcement learning framework for the acceptance strategy in automated negotiations using Deep Q-Network. The training phase is performed multiple times with various reward functions, and the reward capable of a higher utility value is investigated. Simulation experiments with other negotiating agents showed that the proposed method obtained significantly higher utility values than existing methods. ------------------------------ 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.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.2 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Recently, automated negotiation has been attracting attention in multi-agent systems to resolve conflicts and reach an agreement among agents. In automated negotiation, two main types of strategies are incorporated in each agent: a bidding strategy that considers what kind of bid to send to an opponent, and an acceptance strategy that considers whether to accept the opponent's offer. In most bilateral multi-issue negotiation, agents take turns sending bids to each other and the negotiation ends when an agent accepts an opponent's offer. Therefore, the acceptance strategy is important in terms of increasing the utility of an agent. However, most studies of automated negotiation using reinforcement learning focus only on the bidding strategy of the agent, so there are not many studies that investigate acceptance strategies using reinforcement learning. In this paper, we propose a new configuration of a deep reinforcement learning framework for the acceptance strategy in automated negotiations using Deep Q-Network. The training phase is performed multiple times with various reward functions, and the reward capable of a higher utility value is investigated. Simulation experiments with other negotiating agents showed that the proposed method obtained significantly higher utility values than existing methods. ------------------------------ 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.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.2 ------------------------------ |
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| 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 65, 号 1, 発行日 2024-01-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
| 公開者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
