DocumentCode :
2675270
Title :
Dynamic Pricing by Multiagent Reinforcement Learning
Author :
Han, Wei ; Liu, Lingbo ; Zheng, Huaili
Author_Institution :
Inf. Eng. Coll., Nanjing Univ. of Finance & Econ., Nanjing
fYear :
2008
fDate :
3-5 Aug. 2008
Firstpage :
226
Lastpage :
229
Abstract :
Dynamic pricing in electronic marketplaces is a basic problem in electronic commercial. In multiagent environments, the optimal pricing policy of agent depends on the pricing policies of other agents. This makes the learning problem more problematic. This paper proposes an efficient online learning algorithm, which integrates the observed objective actions as well as the subjective inferential intention of the opponents. By establishing the decision model of other agents and predicting their proposed price in advance, agent becomes adaptive to its opponents and can make good decisions in long terms. The algorithm is proven to be effective when coming to the problem of seller´s pricing in electronic marketplaces.
Keywords :
electronic commerce; learning (artificial intelligence); multi-agent systems; pricing; dynamic pricing; electronic commerce; electronic marketplaces; multiagent reinforcement learning; online learning algorithm; optimal pricing policy; Consumer electronics; Economic forecasting; Educational institutions; Electronic commerce; Environmental economics; Finance; Games; Information security; Learning; Pricing; dynamic pricing; electronic marketplaces; multiagent learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Commerce and Security, 2008 International Symposium on
Conference_Location :
Guangzhou City
Print_ISBN :
978-0-7695-3258-5
Type :
conf
DOI :
10.1109/ISECS.2008.179
Filename :
4606060
Link To Document :
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