Information classification using fuzzy knowledge based agents

It is possible to find any kind of useful information in the Web. However, there are serious problems in retrieving, managing and using this information, due to its vastness. Different approaches have been developed to avoid those problems (search engines, metasearch engines, spiders, softbots, intelligent agents or Web agents). This paper is based on one of these systems which uses a set of heterogeneous intelligent software agents to achieve these previous tasks. Two different agents compose the system: Web agents developed to retrieve information from a specific Web source and meta Web agents developed to select the appropriated Web agent to search the necessary information. Each Web agent retrieves, filters and stores the information from the Web to improve system performance. The meta Web agents need to represent and classify the behavior of different Web agents. In this work a fuzzy system that helps to classify the behavior of the Web agents is presented. The meta Web agent calculates the appropriateness of existing agent behavior, using different distances that are analyzed in the paper. The behavior classification is used to decide which Web agent is requested for information by the meta Web agent