Web Directories as a Knowledge Base to Build a Multi-Agent System for Information Sharing

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.