Antomated web navigation using multiagent adaptive dynamic programming

被引:5
作者
Varghese, J [1 ]
Mukhopadhyay, S [1 ]
机构
[1] Indiana Univ, Purdue, IN 46202 USA
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2003年 / 33卷 / 03期
基金
美国国家科学基金会;
关键词
adaptive dynamic programming; multi-agent learning; relevance feedback; vector-space model; Web navigation;
D O I
10.1109/TSMCA.2003.817043
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Today a massive amount of information available on the WWW often makes searching for information of interest a long and tedious task. Chasing hyperlinks to rind relevant information may be daunting. To overcome such a problem, a learning system, cognizant of a user's interests, can be employed to automatically search for and retrieve relevant information by following appropriate hyperlinks. In this paper, we describe the design of such a learning system for automated Web navigation using adaptive dynamic programming methods. To improve the performance of the learning system, we introduce the notion of multiple model-based learning agents operating in parallel, and describe methods for combining their models. Experimental results on the WWW navigation problem are presented to indicate that combining multiple learning agents, relying on user feedback, is a promising direction to improve learning speed in automated WWW navigation.
引用
收藏
页码:412 / 417
页数:6
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