Towards Hybrid Neural Learning Internet Agents

The following chapter explores learning internet agents. In recent years, with the massive increase in the amount of available information on the Internet, a need has arisen for being able to organize and access that data in a meaningful and directed way. Many well-explored techniques from the eld of AI and machine learning have been applied in this context. In this paper, special emphasis is placed on neural network approaches in implementing a learning agent. First, various important approaches are summarized. Then, an approach for neural learning internet agents is presented, one that uses recurrent neural networks for the learning of classifying a textual stream of information. Experimental results are presented showing that a neural network model based on a recurrent plausibility network can act as a scalable, robust and useful news routing agent.

@InCollection{WAP00b,
 	 author =  {Wermter, Stefan and Arevian, Garen and Panchev, Christo},
 	 title = {Towards Hybrid Neural Learning Internet Agents},
 	 booktitle = {Hybrid Neural Systems},
 	 journal = {None},
 	 editors = {Wermter, Stefan; Sun, Ron},
 	 number = {}
 	 volume = {}
 	 pages = {160--176},
 	 year = {2000},
 	 month = {Oct},
 	 publisher = {Springer},
 	 doi = {10.1007/10719871_11},
 }