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Knowledge Processing Method with Calculated Functors

  • Vasily MeltsovEmail author
  • Alexey Kuvaev
  • Natalya Zhukova
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

Intellectual inference methods are among of the convenient tools for solving certain classes of knowledge processing tasks. One of the current areas in which the application of logical inference methods and engines can lead to new results is the field of cyber-physical systems that has been actively developing in recent years, including the control of unmanned vehicles and aircrafts, intelligent mechatronics and robotics. But this requires the operations of processing numerical information to enter into the logical conclusion procedure. The high-performance method of parallel output based on the disjunct (clauses) division is selected as the basic method of logical inference. To implement arithmetic operations, this method is proposed to be supplemented with a special mechanism of calculated functors. The developed modified inference method differs from the known methods by a number of important advantages. Firstly, it will significantly expand the use of artificial intelligence methods in cyber-physical systems. Secondly, inferences and arithmetic operations can be performed in parallel. And thirdly, it is an opportunity to use for the arithmetic calculations the available special processors of logical inference on the FPGA for autonomous intelligent systems for various purposes.

Keywords

Intelligent systems Knowledge processing Logical inference Calculated functors 

References

  1. 1.
    Arseniev, D.G., Lyubimov, B.E., Shkodyrev, V.P.: Intelligent fault detection and diagnostics system on rule-based neural network approach. In: Proceedings of the IEEE International Conference on Control Applications, CCA 2009, no. 5281003 (2009)Google Scholar
  2. 2.
    Arseniev, D.G., Shkodyrev, V.P., Yarotsky, V.A., Yagafarov, K.I.: The model of intelligent autonomous hybrid renewable energy system based on Bayesian network. In: Proceedings of the IEEE 8th International Conference on Intelligent Systems, pp. 758–763 (2016)Google Scholar
  3. 3.
    Bonci, A., Carbonari, A., Cucchiarelli, A., Pirani, M., Vaccarini, M.: A cyber-physical system approach for building efficiency monitoring. Autom. Constr. 102, 68–85 (2019)CrossRefGoogle Scholar
  4. 4.
    Bratko, I.: Prolog Programming for Artificial Intelligence. Addison-Wesley Longman Ltd., Boston (2001)zbMATHGoogle Scholar
  5. 5.
    Dolzhenkova, M.L., Meltsov, V.Yu., Strabykin, D.A.: Method of consequences inference from new facts in case of an incomplete knowledge base. Indian J. Sci. Technol. 9(39), 100413 (2016)Google Scholar
  6. 6.
    Dyachenko, O., Zagorulko, Y.: A collaborative development of ontology-based knowledge bases. Commun. Comput. Inf. Sci. 468, 219–228 (2014)Google Scholar
  7. 7.
    Gavrilova, T., Onufriev, V.: Conceptual modelling: common students’ mistakes in visual representation. In: 20th International Conference on Interactive Collaborative Learning, ICL 2017. Advances in Intelligent Systems and Computing, vol. 716, pp. 199–209 (2018)Google Scholar
  8. 8.
    Hammoudeh, M., Parizi, R., Dehghantanha, A., Xu, Z., Choo, K.-K.R. (ed.): Conference review. In: International Conference on Cyber Security Intelligence and Analytics, CSIA 2019. Advances in Intelligent Systems and Computing, vol. 928 (2019)Google Scholar
  9. 9.
    Levin, I., Dordopulo, A., Fedorov, A., Kalyaev, I.: Reconfigurable computer systems: from the first FPGAs towards liquid cooling systems. Supercomput. Front. Innov. 3–1, 22–40 (2016)Google Scholar
  10. 10.
    Mamoutova, O.V., Shirokova, S.V., Uspenskij, M.B., Loginova, A.V.: The ontology-based approach to data storage systems technical diagnostics. In: E3S Web of Conferences. Topical Problems of Architecture, Civil Engineering and Environmental Economics, TPACEE 2018, vol. 91, no. 080182018 (2019)Google Scholar
  11. 11.
    Meltsov, V.: High-Performance Systems of Deductive Inference. Science Book Publishing House, Yelm (2014)Google Scholar
  12. 12.
    Meltsov, V., Lesnikov, V., Dolzhenkova, M.: Intelligent system of knowledge control with the natural language user interface. In: Proceedings of the 2017 International Conference IT and QM and IS 2017, St. Petersburg, pp. 671–675 (2017)Google Scholar
  13. 13.
    Mikhailov, S., Kashevnik, A.: An ontology for service semantic interoperability in the smartphone-based tourist trip planning system. In: 23rd Conference of Open Innovation Association, FRUCT 2018, pp. 239–245 (2018)Google Scholar
  14. 14.
    Noor, U., Anwar, Z., Amjad, T., Choo, K.: A machine learning-based FinTech cyber threat attribution framework using high-level indicators of compromise. Future Gener. Comput. Syst. 96, 227–242 (2019)CrossRefGoogle Scholar
  15. 15.
    Norvig, P., Russell, S.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Edinburgh (2011)zbMATHGoogle Scholar
  16. 16.
    Osipov, G.S., Panov, A.I.: Relationships and operations in a sign-based world model of the actor. Sci. Techn. Inf. Process. 45(5), 317–330 (2018)CrossRefGoogle Scholar
  17. 17.
    Pospelov, D.: Modeling of deeds in artificial intelligence systems. Appl. Artif. Intell. 7, 15–27 (1993)CrossRefGoogle Scholar
  18. 18.
    Rahman, S.A., Haron, H., Nordin, S., Bakar, A.A., Rahmad, F., Amin, Z.M., Seman, M.R.: The decision processes of deductive inference. Adv. Sci. Lett. 23(1), 532–536 (2017)CrossRefGoogle Scholar
  19. 19.
    Strabykin, D. Inference in knowledge processing systems, St. Petersburg (1998). (in Russian)Google Scholar
  20. 20.
    Strabykin, D.: Logical method for predicting situation development based on abductive inference. J. Comput. Syst. Sci. Int. 52(5), 759–763 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Strabykin, D., Meltsov, V., Dolzhenkova, M., Chistyakov, G., Kuvaev, A.: Formal verification and accelerated inference. In: 5th Computer Science On-line Conference, CSOC 2016. Advances in Intelligent Systems and Computing, vol. 464, pp. 203–211 (2016)Google Scholar
  22. 22.
    Sychugov, A.A., Meltsov, V.Yu., Kuvaev, A.S., Grishin, V.M.: Network intrusions detection and prevention method using a team of intelligent agents. J. Mech. Eng. Res. Dev. 42(2), 14–17 (2019)Google Scholar
  23. 23.
    Vagin, V., Derevyanko, A., Kutepov, V.: Parallel-inference algorithms and research of their efficiency on computer systems. Sci. Tech. Inf. Process. 45(5), 368–373 (2018)CrossRefGoogle Scholar
  24. 24.
    Vagin, V., Antipov, S., Fomina, M., Morosin, O.: Application of intelligent data analysis methods for information security problems. In: 2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017. Advances in Intelligent Systems and Computing, vol. 679, pp. 16–25 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vasily Meltsov
    • 1
    Email author
  • Alexey Kuvaev
    • 1
  • Natalya Zhukova
    • 2
  1. 1.Vyatka State UniversityKirovRussia
  2. 2.St. Petersburg Institute for Informatics and Automation of the Russian Academy of SciencesSaint PetersburgRussia

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