A retraining methodology for enhancing agent intelligence
Data mining has proven a successful gateway for discovering useful knowledge and for enhancing business intelligence in a range of application fields. Incorporating this knowledge into already deployed applications, though, is highly impractical, since it requires reconfigurable software architectures, as well as human expert consulting. In an attempt to overcome this deficiency, we have developed Agent Academy, an integrated development framework that supports both design and control of multi-agent systems (MAS), as well as agent training. We define agent training as the automated incorporation of logic structures generated through data mining into the agents of the system. The increased flexibility and cooperation primitives of MAS, augmented with the training and retraining capabilities of Agent Academy, provide a powerful means for the dynamic exploitation of data mining extracted knowledge. In this paper, we present the methodology and tools for agent retraining. Through experimental results with the Agent Academy platform, we demonstrate how the extracted knowledge can be formulated and how retraining can lead to the improvement - in the long run - of agent intelligence.
P. A. Mitkas, A. L. Symeonidis, I. N. Athanasiadis, A retraining methodology for enhancing agent intelligence, IEEE Int'l Conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS-05), pg. 422-428, 2005, IEEE, doi:10.1109/KIMAS.2005.1427118.
You might also enjoy (View all publications)
- Storing, combining and analysing turkey experimental data in the Big Data era
- Enabling reusability of plant phenomic datasets with MIAPPE 1.1
- Defining and classifying infrastructural contestation: Towards a synergy between anthropology and data science