ARCHON: an architecture for multi-agent systems
ARCHON: an architecture for multi-agent systems
Machine Learning
Coordination techniques for distributed artificial intelligence
Foundations of distributed artificial intelligence
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Mining Very Large Databases with Parallel Processing
Mining Very Large Databases with Parallel Processing
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiagent-Based Model Integration
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Integrating knowledge through cooperative negotiation: a case study in bioinformatics
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Improving the efficiency of distributed data mining using an adjustment work flow
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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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.