Methods in Knowledge Management of a Knowledge Based Multiagent System

Multi-agent system is a system of autonomous, intelligent but resource-bounded agents. Particular agents have to be able to make decisions on their own, based on the activities of other agents and events within the system as well as in its environment. These decisions may be based on typical information from relational databases (communication and reasoning based on some logical systems) but also from spatial data that could be obtained from other agents or from agent‟s own perception (data bounded to ontologies). To this end agents make use of their own internal knowledge base which serves them as a memory. This internal knowledge base is at first place build from the system‟s stable part (ontology and its content). Other content of the knowledge base is the relative part which is obtained from communication and reasoning. In this paper we focus on the design and management of such a knowledge base. Knowledge base for knowledge based multi-agent systems purposes must be optimized for big amounts of data so after a brief description of some classical fundamental approaches to the knowledge base management, we propose an improvement based on the application of statistical methods. We focus in particular on the optimization of the process. We also introduce enhanced model of knowledge exchange as a result of the optimization process.