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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References
Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Comput. Math. Appl.: Spec. Issue Comput. Linguist. 9(1), 149–184 (1983)
Ryabinin, I.A.: Reliability and Safety of Structural Complex Systems. Polytechnic, St. Petersburg (2000) (in Russian)
Gorodetskiy, A.E., Tarasova, I.L.: Fuzzy Mathematical Modelling of Poorly Formalized Processes and Systems. SPbSTU Publishing, St.Petersburg (2010) (in Russian)
Vagin, V.N., Golovina, E.Y., Zagoryanskaya, A.A., Fomina, M.V.: Exact and plausible reasoning in intelligent systems. In: Vagin, V.N., Pospelov, D.A.(eds.). Fizmatlit, Moscow (2004) (in Russian)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)
Thayse, A., et al.: Approche Logique de L’intelligence Artificielle Tome 2: De la Logique Modale à la Logique des Bases de Données. Dunod, Paris (1989)
Kulik, B., Zuenko, A., Fridman, A.: An Algebraic Approach to Intelligent Processing of Data and Knowledge. Polytechnic University, Saint Petersburg (2010)
Kulik, B., Fridman, A., Zuenko, A.: Logical inference and defeasible reasoning in N-tuple algebra. In: Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems, pp. 102–128. IGI Global (2013)
Kulik, B.A.: N-Tuple algebra-based probabilistic logic. Syst. Anal. Oper. Res. 46(1), 111–120 (2007)
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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