Gaya López, María CruzGiráldez Betrón, Juan Ignacio2016-07-092016-07-092009Gaya, M. C., & Giráldez, J. I. (2009). Techniques for distributed theory synthesis in multiagent systems. In International Symposium on Distributed Computing and Artificial Intelligence 2008: DCAI 2008 (pp. 395-402). Berlin: Springer. DOI: 10.1007/978-3-540-85863-8_4697835408586219783540858638http://hdl.handle.net/11268/5387Data 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.engconference output10.1007/978-3-540-85863-8_46restricted accessSistemas multiagenteMotores de búsquedaInformática