Robust Methods for Databases and Robotics

C.Rodríguez Lucatero (Mexico), M. de Rougemont (France), and R. Lozano Espinosa (Mexico)


Database, XML, Robot Motion Planning, Complexity. Markovian strategies.


The goal of this article is to propose the development of robust methods for the processing of different data sources that appear in the integration of data bases and in the data fusion in robotics. In the Data Bases field, one wish to classify uncertain data and to answer queries in an approximated way in the case that data sources are incoherent. Our interest is based in the fundamental problems in the domain of XML data as well as in the data flow. Concerning mobile robotics we would like to compare robot strategies in uncertain environments and to learn good strategies in that situations. From one side we have interest on exploring some property proof and learning aspects, and by the other side , we are interested on game theory equilibrium techniques.

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