Learning Mechanism of Automated Negotiation in E-commerce

Author

He, Bo ; Chen, Yuan ; Huang, Xianying ; Yang, Wu

Author_Institution

Dept. of Comput. Sci. & Eng., Chongqing Inst. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4152

Lastpage

4156

Abstract

Aiming at the shortcoming of current automated negotiation systems, this paper applied machine learning to bilateral automated negotiation. It mainly researched learning mechanism of automated negotiation in e-commerce. It improved traditional Q-learning and designed dynamic Q-learning algorithm. This algorithm estimated Q value according to environment state and the action of both agents, furthermore, recency-based exploration bonus were embedded. The paper applied Bayesian learning to negotiation strategy of automated negotiation, and designed the belief strategy based on Bayesian. Finally, this paper did experiments on learning mechanism and dynamic Q-learning algorithm. The results show that learning mechanism can improve efficiency of automated negotiation and dynamic Q-learning algorithm is efficient

Keywords

belief networks; electronic commerce; learning (artificial intelligence); multi-agent systems; negotiation support systems; Bayesian learning; Q-learning algorithm; automated negotiation system; belief strategy; bilateral automated negotiation; e-commerce; learning mechanism; machine learning; recency-based exploration bonus; Algorithm design and analysis; Bayesian methods; Computer science; Helium; Heuristic algorithms; Intelligent control; Learning systems; Machine learning; Machine learning algorithms; State estimation; Automated negotiation; Bayesian learning; E-commerce; Q-learning algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713156

Filename

1713156