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

Takaki MATSUNE; Katsuhide FUJITA

doi:10.1587/transinf.2018EDP7056

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

Takaki MATSUNE, Katsuhide FUJITA

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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

Authors

Takaki MATSUNE
Tokyo University of Agriculture and Technology
Katsuhide FUJITA
Tokyo University of Agriculture and Technology

Keyword

automated multi-issue negotiation, automated negotiating agents competition, multi-time negotiation, multilateral negotiation, boosting

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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 -