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

Takaki MATSUNE  Katsuhide FUJITA  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.10   pp.2474-2484
Publication Date: 2018/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7056
Type of Manuscript: PAPER
Category: Information Network
automated multi-issue negotiation,  automated negotiating agents competition,  multi-time negotiation,  multilateral negotiation,  boosting,  

Full Text: PDF(547.4KB)
>>Buy this Article

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.