By Topic

A neural multi-agent based system for smart HTML pages retrieval

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Pilato, G. ; I.C.A.R., Italian Nat. Res. Council, Palermo, Italy ; Vitabile, S. ; Vassallo, G. ; Conti, V.
more authors

A neural based multi-agent system for smart HTML page retrieval is presented. The system is based on the EalphaNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve documents satisfying a query and dealing with a specific topic. The system has been developed using the basic features supplied by the Jade platform for agent creation, coordination and control. The system is composed of four agents: the trainer agent, the neural classifier mobile agent, the interface agent, and the librarian agent. The sub-symbolic knowledge of the neural classifier mobile agent is automatically updated each time a new, previously not included, document topic is requested by the user. The neural classifier mobile agent also interacts with the librarian agent for retrieving the documents in the repositories and with the interface agent for user interaction. The proposed system is particularly useful for classifying documents stored in private networked document repositories that, for various reasons (i.e. privacy, security, and so on), cannot be indexed by an external search engine. The system is very efficient: the preliminary experimental results show that in the best case a classification error of 9.98% is obtained.

Published in:

Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on

Date of Conference:

17-17 Oct. 2003