Towards hybrid neural learning internet agents

Stefan Wermter, Garen Arevian, Christo Panchev

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

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 field 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. concluding section examines the need for a hybrid integration of various techniques to achieve optimal results in the problem domain specified, in particular exploring the hybrid integration of Preference Moore machines and recurrent networks to extract symbolic knowledge.
Original languageEnglish
Title of host publicationHybrid Neural Systems
EditorsStefan Wermter, Ron Sun
Place of PublicationBerlin
PublisherSpringer Verlag
Pages158-174
Number of pages17
ISBN (Electronic)978-3-540-46417-4
ISBN (Print)978-3-540-67305-7
DOIs
Publication statusPublished - 2000
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
Volume1778

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