Abstract
Developers of intelligence systems to control SEMS often face situations when incompleteness of their knowledge results in uncertain definitions of initial data (for instance, as some hypotheses to be proved or refuted, possible incompatible variants or special cases to be generalized, or probabilistic models). To investigate and consider such uncertainties, many researchers in artificial intelligence use non-classical logics, which violate some laws of algebra of sets. Conversely, in the given paper we propose techniques to analyze data and knowledge with uncertainties within the classical approach by using our earlier developed n-tuple algebra.
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Acknowledgements
The authors would like to thank the Russian Foundation for Basic Researches (grants 13-07-00318, 14-07-00256, 14-07-00257, 14-07-00205, 15-07-04760, and 15-07-02757) for partial funding of this research.
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Kulik, B.A., Fridman, A.Y. (2016). Logical Analysis of Data and Knowledge with Uncertainties in SEMS. In: Gorodetskiy, A. (eds) Smart Electromechanical Systems. Studies in Systems, Decision and Control, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-27547-5_5
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DOI: https://doi.org/10.1007/978-3-319-27547-5_5
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