淡江大學機構典藏:Item 987654321/21091
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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/21091


    Title: Performance Enhancement of Bayesian Learning: An Application involving the Bargaining Agent of an Online Bookstore
    Other Titles: 貝氏學習績效之改善:以線上書店之議價代理人為例
    Authors: 鄭啟斌;Cheng, Chi-bin;詹智強;Chan, C. C. Henry;謝岳峰;Hsieh, Yueh-feng
    Contributors: 淡江大學資訊管理學系
    Keywords: electronic commerce;Bayesian learning;agents;bargaining
    Date: 2007-09-01
    Issue Date: 2009-11-30 13:13:34 (UTC+8)
    Publisher: 中國工業工程學會
    Abstract: E-commerce agents with Bayesian learning were first proposed by Zeng and Sycara in their Bazaar automated bargaining system [18]. Many studies have directly applied or extended Bazaar to agent learning. In Bayesian learning, it is critical to construct the conditional probabilities for new events in order to obtain an accurate estimation of the posterior probability. The construction of such conditional probabilities requires domain knowledge of the target problem and an appropriate translation of this knowledge into a corresponding set of conditional probabilities. Unfortunately, such issues have either been ignored or over-simplified in previous studies. Accordingly, the present study aims to enhance the performance of Bayesian learning by developing a new formulation for the conditional probabilities during the learning process. An online used-textbook store is built and used as the basis for a series of experiments to evaluate the performance of the proposed approach. The experimental results demonstrate that the prediction accuracy of Bayesian learning using the proposed conditional probability formulation is superior to that of a previous approach that uses a simpler formulation of conditional probabilities.
    Relation: 工業工程學刊24(5),頁388-396
    DOI: 10.1080/10170660709509054
    Appears in Collections:[Graduate Institute & Department of Information Management] Journal Article

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