Robust Characterizations of Polynomials withApplications to Program Testing
SIAM Journal on Computing
Regular Languages are Testable with a Constant Number of Queries
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Property testing and its connection to learning and approximation
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
On the value of private information
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Spatial navigation with uncertain deviations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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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 Databases and in the data fusion in robotics. In the Databases 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 on 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.