Design of Knowledge Discovery Agent based on Self Adaptive Components

Abstract

Agents can be autonomous entities, deciding their next step without the interference of a user or they can be controllable ,serving as a me diary between the user and another agent, thereby having some amount of Artificial Intelligence. Knowledge discovering agents are a very interesting approach to software development in computing environments. An agent consists in a thread of execution that can migrate across the network. In this paper, we will employ a multi-agent for the search and extraction of data in a distributed environment. We will use an Integrator Agent in the proposed model on the Knowledge Discovery Agent(KDA). The KDA will address the inadequacy of other data mining tools in processing performance and efficiency when use for knowledge discovery. The Integrator Agent was developed based on CORBA architecture for search and extraction of data from heterogeneous servers in the distributed environment. Our experiment shows that the KDA generated essential association rules which can be practically explained for decision making purposes.