一种基于奖励机制的智能元搜索引擎

针对元搜索引擎中返回大量重复冗余信息导致结果显示代理负担加大、系统查准率降低的缺陷，结合Agent技术建立基于多Agent的元搜索引擎系统模型，从成员Agent的爬行能力值、检索文档与查询主题的相关度和查询响应时间三个方面综合衡量成员搜索引擎对于查询的重要度，并按降序排序，优先选择重要度最佳的若干成员搜索引擎进行智能调度和智能结果合成。实验结果表明，与传统元搜索引擎相比，这种基于奖励机制的智能元搜索引擎提高了检索效率和查询性能。

An intelligent meta search engine based on reward mechanism

To settle the defects that the repetitive and redundant information returned by meta search engine leads to increase the burden of result display agent and reduce the precision, a meta search en- gine system model based on multi -agent is brought forward. The importance degree of member search engines to a particular query comprehensively is measured, in the aspects of member Agent＇s crawling capability, the relevance between retrieve documents and certain query, and response time during query. Then the importance degrees of member search engines are ordered by descend, so as to select several member search engines with higher importance degree to dispatch and merge. It has proved through the experimental comparison that compared to the traditional meta search engine, the intelligent meta search engine based on reward mechanism improves the retrieval efficiency and query performance to a certain extent.