Encyclopedia of Computer Graphics and Games

Living Edition
| Editors: Newton Lee

2-Simplex Prism as a Cognitive Graphics Tool for Decision-Making

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-08234-9_285-1

Synonyms

Definition

The following definitions from the papers (Yankovskaya 2011; Yankovskaya et al. 2015a) are used:

A cognitive graphics tool (CGT) visually reflects a complex object, phenomenon, or process on a computer screen, enabling the users to form a new decision, idea, or hypothesis based on the visuals.

2-Simplex is an equilateral triangle.

3-Simplex is a regular tetrahedron.

2-Simplex prism is a triangular prism which has identical equilateral triangles (2-simplexes) in its bases.

The height of the 2-simplex prism (Yankovskaya et al. 2015a) in intelligent dynamic systems corresponds to the dynamic process time interval under consideration. It is divided into a number of time intervals. The number of time intervals corresponds to the number of diagnostic or predictive decisions.

The distance between two adjacent 2-simpleces is proportional to...

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Notes

Acknowledgment

The research was funded by RFBR grant (Project No. 16-07-0859a).

References

  1. Albu, V.А., Khoroshevskiy, V.F.: COGR – cognitive graphics system, design, development, application. In: Russian Academy of Science Bulletin, Technical Cybernetics, pp. 12–20, 1990 (in Russian)Google Scholar
  2. Axelrod, R.M.: The Structure of Decision: Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)Google Scholar
  3. Kobrinskiy, B.A.: Why should we take in account imaginary thinking and intuition in medical expert systems. In: Artificial Intelligence – 96, Proceedings of the 5th National Conference with International Participation, pp. 207–210, 1996 (in Russian)Google Scholar
  4. Pospelov, D.A.: Cognitive graphics – a window into the new world. In: Software Products and Systems, pp. 4–6, 1992 (in Russian)Google Scholar
  5. Raciborski, R.: Graphical representation of multivariate data using Chernoff faces. Stata J. 9(3), 374 (2009)CrossRefGoogle Scholar
  6. Ryumkin, A., Yankovskaya, A.: Intelligent expansion of the geoinformation system. In: The 6th German-Russian Workshop “Pattern Recognition and Image Understanding” OGRW-6-2003, Workshop Proceedings, Russian Federation, Novosibirsk, pp. 202–205, 2003Google Scholar
  7. Saary, M.J.: Radar plots: a useful way for presenting multivariate health care data. J. Clin. Epidemiol. 61(4), 311–317 (2008)CrossRefGoogle Scholar
  8. Wang, B., Feng, X., Chu, K.H.: A novel graphical procedure based on ternary diagram for minimizing refinery consumption of fresh hydrogen. J. Clean. Prod. 37, 202–210 (2012)CrossRefGoogle Scholar
  9. Yankovskaya, A.E.: Transformation feature space into pattern space on the base of logic-combinatorial methods and properties of some geometric objects. In: Pattern Recognition and Image Analysis, Minsk, pp. 178–181, 1991Google Scholar
  10. Yankovskaya, A.E.: Design of optimal mixed diagnostic test with reference to the problems of evolutionary computation. In: Proceedings of the First International Conference on Evolutionary Computation and Its Applications (EVCA’96), Moscow, pp. 292–297, 1996Google Scholar
  11. Yankovskaya, A.: Decision-making and decision-justification using cognitive graphics methods based on the experts of different qualification. In: Russian Academy of Science Bulletin, Theory and Control Systems, vol. 5, pp. 125–128, 1997Google Scholar
  12. Yankovskaya, A.: Logical Tests and Means of Cognitive Graphics. LAP LAMBERT Academic Publishing, Saarbrigge (2011)Google Scholar
  13. Yankovskaya, A.: 2-Simplex prism as a cognitive graphic tool for decision-making and its justification in intelligent dynamic and geoinformation systems. In: Proceedings of 4th International Conference “Computer Graphics and Animation,” p. 42, 2017Google Scholar
  14. Yankovskaya, A., Galkin, D.: Cognitive computer based on n-m multiterminal networks for pattern recognition in applied intelligent systems. In: Proceedings of Conference GraphiCon’2009, MAKS Press, Moscow, pp. 299–300, 2009. ISBN 978-5-317-02975-3Google Scholar
  15. Yankovskaya, A., Semenov, M.: Computer based learning by means of mixed diagnostic tests, threshold function and fuzzy logic. In: Proceedings of the IASTED International Conference on Human–Computer Interaction, Baltimore, pp. 218–225, 2012Google Scholar
  16. Yankovskaya, A.E., Sukhorukov, A.V.: Complex matrix model for data and knowledge representation for road-climatic zoning of the territories and the results of its approbation. In: International Conference Information Technology and Nanotechnology, Samara, pp. 264–270, 2017Google Scholar
  17. Yankovskaya, A., Yamshanov, A.: Bases of intelligent system creation of decision-making support on road-climatic zoning. In: Pattern Recognition and Information Processing (PRIP’2014): Proceedings of the 12th International Conference, UIIP NASB, Minsk, vol. 340, pp. 311–315, 28–30 May 2014Google Scholar
  18. Yankovskaya, A., Yamshanov, A.: Family of 2-simplex cognitive tools and their applications for decision-making and its justification. In: Computer Science and Information Technology (CS & IT), pp. 63–76, 2016Google Scholar
  19. Yankovskaya, A.E, Gedike, A.I., Ametov, R.V.: Intelligent dynamic system. In: Knowledge-Dialog-Solution (KDS-2001), Proceedings of International Science –Practical Conference, vol. 2, Pub. “Lan,” Saint-Petersburg, pp. 645–652, 2001Google Scholar
  20. Yankovskaya, A.E., Gedike, A.I., Ametov, R.V., Bleikher, A.M.: IMSLOG-2002 software tool for supporting information technologies of test pattern recognition. In: Pattern Recognition and Image Analysis, vol. 13, no. 4, pp. 650–657, 2003Google Scholar
  21. Yankovskaya, A., Yamshanov, A., Krivdyuk, N.: 2-Simplex prism – a cognitive tool for decision-making and its justifications in intelligent dynamic systems. In: Book of Abstracts of the 17th All-Russian Conference with International Participation: Mathematical Methods for Pattern Recognition, Svetlogorsk, p. 83, 2015aGoogle Scholar
  22. Yankovskaya, A., Dementyev, Y., Yamshanov, A.: Application of learning and testing intelligent system with cognitive component based on mixed diagnostics tests. In: Procedia – Social and Behavioral Sciences, Tomsk, vol. 206, pp. 254–261, 2015bGoogle Scholar
  23. Yankovskaya, A., Dementyev, Y., Yamshanov, A., Lyapunov, D.: Prediction of students’ learning results with usage of mixed diagnostic tests and 2-simplex prism. In: Intelligent Data Processing: Theory and Applications: Book of Abstracts of the 11th International Conference (Moscow/Barcelona), Torus Press, Moscow, vol. 238 pp. 44–45, 2016aGoogle Scholar
  24. Yankovskaya, A., Dementyev, Y., Lyapunov, D., Yamshanov, A.: Intelligent information technology in education. In: Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM), Atlantis Press, Tomsk, vol. 51, pp. 17–21, 2016bGoogle Scholar
  25. Yankovskaya, A.E., Dementev, Y.N., Lyapunov, D.Y., Yamshanov, A.V.: Learning outcomes evaluation based on mixed diagnostic tests and cognitive graphic tools. In: Proceedings of the XVIIth International Conference on Linguistic and Cultural Studies: Traditions and Innovations, Advances in Intelligent Systems and Computing (LKTI 2017), Tomsk, vol. 677, pp. 81–90, 11–13 Oct 2017aGoogle Scholar
  26. Yankovskaya, A.E., Shelupanov, A.A., Shurygin, Y.A., Dementiev, Y.N., Yamshanov, A.V., Lyapunov, D.Y.: Intelligent learning and testing predictive system with cognitive component. In: Open Semantic Technologies for Intelligent Systems, pp. 199–204, 2017bGoogle Scholar
  27. Yankovskaya, A., Dementyev, Y., Yamshanov, A., Lyapunov, D.: Assessing student learning outcomes using mixed diagnostic tests and cognitive graphic tools. In: Open Semantic Technologies for Intelligent Systems: Proceedings of International Conference, Minsk, vol. 2, pp. 351–354, 15–17 Feb 2018 (ISSN 2415–7740. http://proc.ostis.net)
  28. Zenkin, A.A.: Cognitive Computer Graphics. Nauka, Moscow (1991). in RussianzbMATHGoogle Scholar
  29. Zhuravlev, Y.I., Gurevitch, I.B.: Pattern recognition and image analysis. In: Pospelov, D.A. (ed.) Artificial Intelligence in 3 Books, Book 2: Models and Methods: Reference Book, p. 149191. Radio and Comm, Moscow (1990). in RussianGoogle Scholar

Authors and Affiliations

  1. 1.Tomsk State University of Architecture and BuildingTomskRussia
  2. 2.National Research Tomsk Polytechnic UniversityTomskRussia
  3. 3.National Research Tomsk State UniversityTomskRussia
  4. 4.Tomsk State University of Control Systems and RadioelectronicsTomskRussia