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

Takaki MATSUNE, Katsuhide FUJITA

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Summary :

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