Web directories as a knowledge base to build a multi-agent system for information sharing

  • Authors:

  • Giovanni Pilato;Salvatore Vitabile;Giorgio Vassallo;Vincenzo Conti;Filippo Sorbello

  • Affiliations:

  • ICAR - Istituto di calcolo e reti ad alte prestazioni, Sezione di Palermo - Italian National Research Council, Viale delle Scienze, 90128 Palermo, Italy. E-mail: {g.pilato, s.vitabile}@icar.cnr.it;ICAR - Istituto di calcolo e reti ad alte prestazioni, Sezione di Palermo - Italian National Research Council, Viale delle Scienze, 90128 Palermo, Italy. E-mail: {g.pilato, s.vitabile}@icar.cnr.it;DINFO - Dipartimento di ingegneria informatica - University of Palermo, Viale delle Scienze, 90128 Palermo, Italy. E-mail: conti@csai.unipa.it, {gvassallo, sorbello}@unipa.it;DINFO - Dipartimento di ingegneria informatica - University of Palermo, Viale delle Scienze, 90128 Palermo, Italy. E-mail: conti@csai.unipa.it, {gvassallo, sorbello}@unipa.it;DINFO - Dipartimento di ingegneria informatica - University of Palermo, Viale delle Scienze, 90128 Palermo, Italy. E-mail: conti@csai.unipa.it, {gvassallo, sorbello}@unipa.it

  • Venue:
  • Web Intelligence and Agent Systems
  • Year:
  • 2004

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Abstract

A neural based multi-agent system, exploiting the Web Directories as a Knowledge Base for information sharing and documents retrieval, is presented. The system is based on the EαNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve, among documents shared by a networked community, documents satisfying a query and dealing with a specific topic. The system is composed by four agents: the Trainer Agent, the Neural Classifier Agent, the Interface Agent, and the Librarian Agent. The sub-symbolic knowledge of the Neural Classifier Agent is automatically updated each time a new, not included before, document topic is requested by users. The system is very efficient: the experimental results show that, in the best case, a classification error about 10% is obtained.