文章基本信息

标题：CBR BASED LEARNING IN E-AUCTIONS
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作者：Saroj Kaushik ; Himanshu Tagra
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2007
卷号：3
期号：1
页码：18-29
出版社：Journal of Theoretical and Applied
摘要：The advent of e-commerce has brought about a radical change in the process of auctions that can be achieved by using agents. One of the important capabilities of agent is learning from the environment. In this paper, the authors are proposing case based learning for agents in online e-auctions. Case-based reasoning (CBR) is a problem solving paradigm based on the principle that similar problems have similar solutions has inherent learning capability. In auctions, CBR has been proposed to store past histories of similar auctions with their solutions which helps agent to learn from past experience. Proposed bidding agent that uses CBR (CBR-agent) participates in the auction and performs better than those bidders who have no past knowledge about similar auctions. Empirical evaluation is done and the performance of CBR-agent is calculated by comparing its success percentage and average winning price with that of other agents participating in auctions.
关键词：Learning ; Case-based reasoning ; E-Commerce ; Agents