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.
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References
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)
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)
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)
Bratko, I.: Prolog Programming for Artificial Intelligence. Addison-Wesley Longman Ltd., Boston (2001)
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)
Dyachenko, O., Zagorulko, Y.: A collaborative development of ontology-based knowledge bases. Commun. Comput. Inf. Sci. 468, 219–228 (2014)
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)
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)
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)
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)
Meltsov, V.: High-Performance Systems of Deductive Inference. Science Book Publishing House, Yelm (2014)
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)
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)
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)
Norvig, P., Russell, S.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Edinburgh (2011)
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)
Pospelov, D.: Modeling of deeds in artificial intelligence systems. Appl. Artif. Intell. 7, 15–27 (1993)
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)
Strabykin, D. Inference in knowledge processing systems, St. Petersburg (1998). (in Russian)
Strabykin, D.: Logical method for predicting situation development based on abductive inference. J. Comput. Syst. Sci. Int. 52(5), 759–763 (2013)
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)
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)
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)
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)
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Meltsov, V., Kuvaev, A., Zhukova, N. (2020). Knowledge Processing Method with Calculated Functors. In: Arseniev, D., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds) Cyber-Physical Systems and Control. CPS&C 2019. Lecture Notes in Networks and Systems, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-030-34983-7_19
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