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Multi-agent learning approach to WWW information retrieval using neural network

In this paper, we propose a multi-agent learning approach to information retrieval on the World Wide Web where each agent collaboratively learns its environment from user’s relevance feedback using a neural network mechanism. Our approach makes it possible to discover information sources that will give the desired information, and retrieve that information efficiently and effectively. First, we present a framework for our multi-agent learning approach and introduce a training procedure for capturing knowledge about user’s interests and preferences in the information retrieval domain. Secondly, we mathematically analyze the performance of our approach. Finally, to show the utility of our approach, we present the experimental results of our approach and compare them to those obtained by a traditional search service on the World Wide Web.