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Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.10 pp.2474-2484

- Publication Date
- 2018/10/01

- Publicized
- 2018/07/10

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.2018EDP7056

- Type of Manuscript
- PAPER

- Category
- Information Network

Takaki MATSUNE

Tokyo University of Agriculture and Technology

Katsuhide FUJITA

Tokyo University of Agriculture and Technology

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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Takaki MATSUNE, Katsuhide FUJITA, "Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 10, pp. 2474-2484, October 2018, doi: 10.1587/transinf.2018EDP7056.

Abstract: Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7056/_p

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@ARTICLE{e101-d_10_2474,

author={Takaki MATSUNE, Katsuhide FUJITA, },

journal={IEICE TRANSACTIONS on Information},

title={Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations},

year={2018},

volume={E101-D},

number={10},

pages={2474-2484},

abstract={Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.},

keywords={},

doi={10.1587/transinf.2018EDP7056},

ISSN={1745-1361},

month={October},}

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TY - JOUR

TI - Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations

T2 - IEICE TRANSACTIONS on Information

SP - 2474

EP - 2484

AU - Takaki MATSUNE

AU - Katsuhide FUJITA

PY - 2018

DO - 10.1587/transinf.2018EDP7056

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E101-D

IS - 10

JA - IEICE TRANSACTIONS on Information

Y1 - October 2018

AB - Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

ER -