Using Distributed Data Mining and Distributed Artificial Intelligence for Knowledge Integration

In this paper we study Distributed Data Mining from a Distributed Artificial Intelligence perspective. Very often, databases are very large to be mined. Then Distributed Data Mining can be used for discovering knowledge (rule sets) generated from parts of the entire training data set. This process requires cooperation and coordination between the processors because incon-sistent, incomplete and useless knowledge can be generated, since each processor uses partial data. Cooperation and coordination are important issues in Distributed Artificial Intelligence and can be accomplished with different techniques: planning (centralized, partially distributed and distributed), negotiation, reaction, etc. In this work we discuss a coordination protocol for cooperative learning agents of a MAS developed previously, comparing it conceptually with other learning systems. This cooperative process is hierarchical and works under the coordination of a manager agent. The proposed model aims to select the best rules for integration into the global model without, however, decreasing its accuracy rate. We have also done experiments comparing accuracy and complexity of the knowledge generated by the cooperative agents.