
For FullText PDF, please login, if you are a member of IEICE,
or go to Pay Per View on menu list, if you are a nonmember of IEICE.

Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in MultiTime Negotiations
Takaki MATSUNE Katsuhide FUJITA
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E101D
No.10
pp.24742484 Publication Date: 2018/10/01
Online ISSN: 17451361
DOI: 10.1587/transinf.2018EDP7056
Type of Manuscript: PAPER Category: Information Network Keyword: automated multiissue negotiation, automated negotiating agents competition, multitime negotiation, multilateral negotiation, boosting,
Full Text: PDF(547.4KB) >>Buy this Article
Summary:
Recently, multiissue closed negotiations have attracted attention in multiagent systems. In particular, multitime and multilateral negotiation strategies are important topics in multiissue closed negotiations. In multiissue 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 leastsquares 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 frequencybased 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.

