Dynamically organizing KDD processes

How to increase both autonomy and versatility of a knowledge discovery syst em is a core problem and a crucial aspect of KDD (Knowledge Discovery and D ata Mining). Within the framework of the KDD process and the GLS (Global Le arning Scheme) system recently proposed by us, this paper describes a way o f increasing both autonomy and versatility of a KDD system by dynamically o rganizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontolog y to describe KDD agents, in the style of GOER (Object Oriented Entity Rela tionship) data model. Based on this ontology of KDD agents, we apply severa l AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, know ledge and process. The GLS system, as a multistrategy and multiagent KDD sy stem based on the methodology, increases both autonomy and versatility.