Data sources are often dispersed geographically in real life applications. Finding a knowledge model may require to join all the data sources and to run a machine learning algorithm on the joint set. We present an alternative based on a Multi Agent System (MAS): an agent mines one data source in order to extract a local theory (knowledge model) and then merges it with the previous MAS theory using a knowledge fusion technique. This way, we obtain a global theory that summarizes the distributed knowledge without spending resources and time in joining data sources. New experiments have been executed including statistical significance analysis. The results show that, as a result of knowledge fusion, the accuracy of initial theories is significantly improved as well as the accuracy of the monolithic solution. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Gaya, M. C., & Giráldez, J. I. (2009). Techniques for distributed theory synthesis in multiagent systems. In Advances in Soft Computing (Vol. 50, pp. 395–402). https://doi.org/10.1007/978-3-540-85863-8_46
Mendeley helps you to discover research relevant for your work.