ТРЕХУРОВНЕВАЯ МОДЕЛЬ ПРЕДСТАВЛЕНИЯ ЗНАНИЙ НА ОСНОВЕ ГРАФОВ
Ключевые слова:
Нечеткий граф, модель представления знаний, сложная техническая система, данные, информация, знания
Аннотация
Данная статья посвящена решению важной и актуальной задачи представления зна-ний в сложных технических системах. Выполнен обзор существующих подходов к пред-ставлению знаний. Обосновано использование трехуровневой структуры представления знаний в заданной предметной области, выполнено ее формальное описание. Разработана трехуровневая модель представления знаний на основе графов, в которой на первом уровне формируются данные, на втором уровне – информация, а на третьем уровне производятся знания. Приведено формальное описание нечеткого графа, предназначенного для моделиро-вания сложных технических систем. В качестве сложной технической системы рассмат-ривается система охраны протяженного периметра. К объектам системы охраны пери-метра относятся стационарные объекты охраны, технические устройства, предназна-ченные для наблюдения за объектами, и потенциальные нарушители. Вершины графа со-ответствуют объектам системы охраны, а связи графа представляют собой разнотип-ные отношения между объектами. Для моделирования систем с разнотипными информа-ционными потоками обосновано использование графа, который позволяет учесть сочета-ние однотипных, разнотипных и множественных связей в виде вектора. В программном комплексе автора разработана трехуровневая модель на основе нечеткого графа, где дан-ные представлены вершинами, информация – однотипными связями, а знания представля-ют собой множество путей в графе, ведущих к некоему результату. В результате моде-лирования выполнен расчет числа технических устройств, необходимых для охраны задан-ного периметра, и решена задача распределения зон влияния технических устройств на объекты системы охраны. Результаты экспериментальных исследований продемонстри-ровали, что предложенные автором подходы позволяют снизить время получения знаний в графах до 1000 вершин на 16 ms.
Литература
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19. Muntyan E.R. Realizatsiya nechetkoy modeli vzaimodeystviya ob"ektov slozhnykh tekhnicheskikh sistem na osnove grafov [Realization of fuzzy model of objects interaction in-side a complex technical systems based on graphs], Programmnye produkty i sistemy [Soft-ware & Systems], 2019, Vol. 32, No. 3, pp. 411-418. DOI:10.15827/0236-235X.127.411-418.
20. Ore O. Teoriya grafov [Theory of graphs]. Moscow: Nauka, 1980, 336 p.
21. Gladkov L.A., Kureychik V.V., Kureychik V.M. Diskretnaya matematika: uchebnik [Discrete mathematics: textbook]. Moscow: Fizmatlit, 2014, 496 p.
22. Averkin A.N., Batyrshin I.Z. i dr. Nechetkie mnozhestva v modelyakh upravleniya iskusstvennogo intellekta [Fuzzy sets in control models of artificial intelligence], ed. by D.A. Pospelova. Moscow: Nauka, 1986.
23. Yarushkina N.G. Osnovy teorii nechetkikh i gibridnykh system [Fundamentals of the theory of fuzzy and hybrid systems]. Moscow: Finansy i statistika, 2004, 320 p.
24. Latour B. Politics of nature. How to bring the sciences into democracy. Cambridge, Harvard University Press, 2004, 320 p.
25. Muntyan E.R. Programmnyy modul' dlya predstavleniya aktorov i otnosheniy mezhdu aktorami na osnove grafov. Svid. o gos. registr. progr. dlya EVM № 2018665499; zaregistr. 05.12.2018 [Certificate of official registration of a computer program No. 2018665499, “A software module for representing actors and relationships between actors based on graphs”, registered 05.12.2018]. Moscow: Rospatent, 2018.
26. Muntyan E.R. Razrabotka programmnogo kompleksa dlya modelirovaniya vzaimodeystviya aktorov i ikh grupp na osnove grafov [Development of a software complex for modeling inter-action of actors and their groups on the basis of graphs], Innovatsionnye tekhnologii i didaktika v obuchenii: Cb. statey mezhdunarodnoy nauchno-prakticheskoy konferentsii [Innovative technologies and didactics in teaching: Collection of articles of the international scientific and practical conferenceъ. Taganrog: Izd-vo YuFU, 2018, T. 1, pp. 74-78.
27. Sergeev N.E., Muntyan E.R. Primenenie algoritma svertki dlya razdeleniya grafa na proportsional'nye podgrafy [Application of the convolution algorithm to separate a graph into pro-portional subgraphs], Vestnik UGATU [Vestnik USATU], 2018, Vol. 22, No. 1 (79), pp. 121-130.
28. Kolodenkova A.E., Muntyan E.R., Korobkin V.V. Modern approaches to modeling of risk situa-tions during creation complex technical systems, Advances in Intelligent Systems and Computting, 2019, Vol. 875, pp. 209-217. DOI: 10.1007/978-3-030-01821-4_22.
29. Muntyan E.R. Programmnyy modul' dlya modelirovaniya vzaimodeystviya aktorov i grupp aktorov na osnove grafov. Svid. o gos. registr. progr. dlya EVM № 2018665593; zareg. 06.12.2018 [Certificate of official registration of a computer program No. 2018665593, “Soft-ware module for modeling interaction between actors and groups of actors based on graphs”, registered 06.12.2018]. Moscow: Rospatent, 2018.
2. Bronfel'd G.B. Nekotorye vozmozhnosti formal'nogo predstavleniya struktury znaniy [Some possibilities of formal representation of knowledge structure], Tr. Nizhegorodskogo gosudarstvennogo tekhnicheskogo universiteta im. R.E. Alekseeva [Proc. of the Nizhny Nov-gorod state technical University named after R.E. Alekseev], 2012, No. 4 (97), pp. 91-100.
3. Vagin V.N. Znanie v intellektual'nykh sistemakh [Knowledge in intelligent system], Novosti iskusstvennogo intellekta [Artificial intelligence news], 2002, No. 6 (54), pp. 8-18.
4. Kondrashina E.Yu., Litvintseva L.V., Pospelov D.A. Predstavlenie znaniy o vremeni i prostranstve v intellektual'nykh sistemakh [Representation of knowledge about time and space in intellectual systems], ed. by D.A. Pospelova. Moscow: Nauka, 1989, 328 p.
5. Tuomi I. Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory, Journal of Management Information Sys-tems, 1999, No 16 (3), pp. 103-107.
6. Zeleny M. Human systems management: integrating knowledge, Management and Systems, World Scientific, 2005, pp. 15-16.
7. Cleveland H. Information as a resource, The Futurist, December 1982, pp. 34-39.
8. Ackoff R. From data to wisdom, Journal of Applied Systems Analysis, 1989, No.o 16, pp. 3-9.
9. Carpenter, S.A. and Cannady J. Tool for sharing and assessing models of fusion-based space transportation systems, Proc. of the 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Fort Lauderdale, Florida, 2004.
10. Minsky M. A Framework for representing knowledge, Patrick Henry Winston (ed.), The Psy-chology of Computer Vision, New York, 1975.
11. Ueno Kh., Koyamo T., Okamoto T., Isidzuka M. Predstavlenie i ispol'zovanie znaniy [Presenta-tion and use of knowledge], ed. by Kh. Ueno, M. Isidzuka. Moscow: Mir, 1989, 220 p.
12. Avdeenko T.V., Makarova E.S. Sistema podderzhki prinyatiya resheniy v IT-podrazdeleniyakh na osnove integratsii pretsedentnogo podkhoda i ontologii [Decision support system in IT de-partments based on integration of case-based approach and ontology], Vestnik AGTU [Vestnik ASTU], 2017, No. 3, pp. 85-99.
13. Wanga H., Kwonga S., Jinb Y., Wei W., Man K.F. Multiobjective hierarchical genetic algo-rithm for interpretable fuzzy rule-based knowledge extraction, Fuzzy Sets and System, 2005, No. 149.
14. Muntyan E.R. Ispol'zovanie gibridnykh podkhodov dlya predstavleniya znaniy [Using hybrid approaches for knowledge representation], Gibridnye i sinergeticheskie intellektual'nye sistemy: Mater. IV Vserossiyskoy Pospelovskoy konferentsii s mezhdunarodnym uchastiem [Hybrid and synergetic intellectual systems, Proc. of the IV all-Russian Pospelov conference with international participation]. Kaliningrad, Izd-vo BFU im. I. Kanta, 2018, pp. 199-203.
15. Bashlykov A.A., Eremeev A.P. Osnovy konstruirovaniya intellektual'nykh sistem podderzhki prinyatiya resheniy v atomnoy energetike: uchebnik [. Fundamentals of designing intelligent decision support systems in nuclear power: textbook]. Moscow: INFRA-M, 2017, 351 p.
16. Eremeev A., Varshavskiy P., Alekhin R. Case-based reasoning module for intelligent decision sup-port systems, Proc. of the First International Scientific Conference «Intelligent Information Tech-nologies for Industry» (IITI’16), Vol. 1, Pt. 3. Springer International Publ., 2016, pp. 207-216.
17. Kolodenkova A.E., Vereshchagina S.S., Muntyan E.R. Razrabotka edinoy intellektual'noy sistemy podderzhki prinyatiya resheniy dlya diagnostirovaniya elektrotekhnicheskogo oborudovaniya promyshlennosti [Development of a unified intelligent decision support system for diagnosing electrical equipment in the industry], Tr. XIII Vserossiyskogo soveshchaniya po problemam upravleniya VSPU-2019 [Proc. of the XIII all-Russian conference on control prob-lems VCPU-2019]. Moscow: IPU RAN, 2019, pp. 1874-1878.
18. Semenov D.A. Modelirovanie strukturirovannogo obsuzhdeniya zadachi na osnove ekspertnykh grafov [Modeling of structured problem discussion based on expert graphs], Sovremennye informatsionnye tekhnologii i IT-obrazovanie [Modern information technologies and IT-education], 2011, pp. 746-756.
19. Muntyan E.R. Realizatsiya nechetkoy modeli vzaimodeystviya ob"ektov slozhnykh tekhnicheskikh sistem na osnove grafov [Realization of fuzzy model of objects interaction in-side a complex technical systems based on graphs], Programmnye produkty i sistemy [Soft-ware & Systems], 2019, Vol. 32, No. 3, pp. 411-418. DOI:10.15827/0236-235X.127.411-418.
20. Ore O. Teoriya grafov [Theory of graphs]. Moscow: Nauka, 1980, 336 p.
21. Gladkov L.A., Kureychik V.V., Kureychik V.M. Diskretnaya matematika: uchebnik [Discrete mathematics: textbook]. Moscow: Fizmatlit, 2014, 496 p.
22. Averkin A.N., Batyrshin I.Z. i dr. Nechetkie mnozhestva v modelyakh upravleniya iskusstvennogo intellekta [Fuzzy sets in control models of artificial intelligence], ed. by D.A. Pospelova. Moscow: Nauka, 1986.
23. Yarushkina N.G. Osnovy teorii nechetkikh i gibridnykh system [Fundamentals of the theory of fuzzy and hybrid systems]. Moscow: Finansy i statistika, 2004, 320 p.
24. Latour B. Politics of nature. How to bring the sciences into democracy. Cambridge, Harvard University Press, 2004, 320 p.
25. Muntyan E.R. Programmnyy modul' dlya predstavleniya aktorov i otnosheniy mezhdu aktorami na osnove grafov. Svid. o gos. registr. progr. dlya EVM № 2018665499; zaregistr. 05.12.2018 [Certificate of official registration of a computer program No. 2018665499, “A software module for representing actors and relationships between actors based on graphs”, registered 05.12.2018]. Moscow: Rospatent, 2018.
26. Muntyan E.R. Razrabotka programmnogo kompleksa dlya modelirovaniya vzaimodeystviya aktorov i ikh grupp na osnove grafov [Development of a software complex for modeling inter-action of actors and their groups on the basis of graphs], Innovatsionnye tekhnologii i didaktika v obuchenii: Cb. statey mezhdunarodnoy nauchno-prakticheskoy konferentsii [Innovative technologies and didactics in teaching: Collection of articles of the international scientific and practical conferenceъ. Taganrog: Izd-vo YuFU, 2018, T. 1, pp. 74-78.
27. Sergeev N.E., Muntyan E.R. Primenenie algoritma svertki dlya razdeleniya grafa na proportsional'nye podgrafy [Application of the convolution algorithm to separate a graph into pro-portional subgraphs], Vestnik UGATU [Vestnik USATU], 2018, Vol. 22, No. 1 (79), pp. 121-130.
28. Kolodenkova A.E., Muntyan E.R., Korobkin V.V. Modern approaches to modeling of risk situa-tions during creation complex technical systems, Advances in Intelligent Systems and Computting, 2019, Vol. 875, pp. 209-217. DOI: 10.1007/978-3-030-01821-4_22.
29. Muntyan E.R. Programmnyy modul' dlya modelirovaniya vzaimodeystviya aktorov i grupp aktorov na osnove grafov. Svid. o gos. registr. progr. dlya EVM № 2018665593; zareg. 06.12.2018 [Certificate of official registration of a computer program No. 2018665593, “Soft-ware module for modeling interaction between actors and groups of actors based on graphs”, registered 06.12.2018]. Moscow: Rospatent, 2018.
