Automated Web navigation using multiagent adaptive dynamic programming

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 find 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.