Skip to main content
Log in

Cognitive Monitoring of Distributed Objects

  • Information Analysis
  • Published:
Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

A new approach is described that allows replacing traditional monitoring with cognitive monitoring. The basic provisions of cognitive monitoring are exposed and its peculiar features identified. A set of nested hierarchical relatively finite operation automatons is proposed for the formal description of cognitive monitoring models. The general cognitive monitoring algorithm is exposed. Quantitative cognitive monitoring efficiency estimates are provided by two key indices, namely the number of simultaneously observed objects and fullness of synthesized object models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Nazarov, A.V., et al., Sovremennaya telemetriya v teorii i na praktike (Modern Telemetry in Theory and Practice), St. Petersburg: Nauka i tekhnika, 2007.

    Google Scholar 

  2. Haykin, S., Fatemi, M., Setoodeh, P., and Xue, Y., Cognitive control, Proceedings of the IEEE, 2012, vol. 100, no. 12, pp. 3156–3169.

    Article  Google Scholar 

  3. Haykin, S., Xue, Y., and Setoodeh, P., Cognitive radar: Step toward bridging the gap between neuroscience and engineering, Proceedings of the IEEE, 2012, vol. 100, no. 11, pp. 3102–3130.

    Article  Google Scholar 

  4. Augello, A., Infantino, I., Pilato, G., and Vella, F., Creativity evaluation in a cognitive architecture, Biol. Inspired Cognit. Archit., 2015, vol. 11, pp. 29–37.

    Article  Google Scholar 

  5. Lieto, A. and Cruciani, M., Introduction to cognitive artificial systems, Connect. Sci., 2015, vol. 27, no. 2, pp. 103–104.

    Article  Google Scholar 

  6. Huang, K., Zhang, R., Jin, X., and Hussain, A., Special issue editorial: Cognitively-inspired computing for knowledge discovery, Cognit. Comput., 2018, vol. 10, no. 1, pp. 1–2.

    Article  Google Scholar 

  7. Kotseruba, I. and Tsotsos, J., Review of 40 years of cognitive architecture research: Core cognitive abilities and practical applications, Artif. Intell. Rev., Int. Sci. Eng. J., 2018, vol. 50, pp. 1–78.

    Article  Google Scholar 

  8. Rvachev, M., Neuron as a reward-modulated combinatorial switch and a model of learning behavior, Neural Networks, 2013, vol. 46, pp. 62–74.

    Article  MATH  Google Scholar 

  9. Spratling, M., A hierarchical predictive coding model of object recognition in natural images, Cognit. Comput., 2017, vol. 9, no. 2, pp. 151–167.

    Article  Google Scholar 

  10. Sun, R., The importance of cognitive architectures: An analysis based on CLARION, J. Exp. Theor. Artif. Intell., 2007, vol. 19, no. 2, pp. 159–193.

    Article  Google Scholar 

  11. Lemaignan, S., Warniera, M., Sisbota, E., Clodica, A., and Alamia, R., Artificial cognition for social humanrobot interaction: An implementation, Artif. Intell., 2017, vol. 247, pp. 45–69.

    Article  Google Scholar 

  12. Lucentini, D. and Gudwin, R., A comparison among cognitive architectures: A theoretical analysis, Procedia Comput. Sci., 2015, vol. 71, pp. 51–61.

    Article  Google Scholar 

  13. Doell, C. and Siebert, S., Evaluation of cognitive architectures inspired by cognitive biases, Procedia Comput. Sci., 2016, vol. 88, pp. 155–162.

    Article  Google Scholar 

  14. Ichise, R., An analysis of the CHC model for comparing cognitive architectures, Procedia Comput. Sci., 2016, vol. 88, pp. 239–244.

    Article  Google Scholar 

  15. Tweedale, J., A review of cognitive decision-making within future mission systems, Procedia Comput. Sci., 2014, vol. 35, pp. 1043–1052.

    Article  Google Scholar 

  16. Glodek, M., Honold, F., Geier, T., Krell, G., Nothdurft, F., Reuter, S., Schüssel, F., Hörnle, T., Dietmayer, K., Minker, W., Biundo, S., Weber, M., Palm, G., and Schwenker, F., Fusion paradigms in cognitive technical systems for human-computer interaction, J. Neurocomput. Arch., 2015, vol. 161, pp. 17–37.

    Article  Google Scholar 

  17. Goertzel, B., Lian, R., Arel, I., Garis, H., and Chen, S., World survey of artificial brains, Part II: Biologically inspired cognitive architectures, J. Neurocomput. Arch., 2010, vol. 74, nos. 1–3, pp. 30–49.

    Article  Google Scholar 

  18. Madla, T., Chena, K., Montaldi, D., and Trappl, R., Computational cognitive models of spatial memory in navigation space: A review, Neural Networks, 2015, vol. 65, pp. 18–43.

    Article  Google Scholar 

  19. Rozenberg, I., Cognitive transport management, Gos. Sov., 2015, no. 2, pp. 47–52.

  20. Zagoruiko, N.G., Kognitivnyi analiz dannykh (Cognitive Data Analysis), Novosibirsk: Geo, 2013.

    Google Scholar 

  21. Wu, O., Ding, G., Yuhua, Xu Y., Feng, S., Du, Z., Wang, J., and Long, K., Cognitive Internet of Things: A new paradigm beyond connection, IEEE Internet Things J., 2014, vol. 1, no. 2.

    Google Scholar 

  22. Sangaiah, A., Thangavelu, A., and Meenakshi Sundaram, V., Cognitive computing for Big Data systems over IoT frameworks, tools and applications, Lect. Notes Data Eng. Commun. Technol., Frameworks Tools Appl., 2018, vol. 14.

  23. Fel’dbaum, A., Theory of dual control. IV, Avtom. Telemekh., 1961, vol. 22, no. 2, pp. 129–142.

    MathSciNet  MATH  Google Scholar 

  24. Okhtilev, M.Yu., Sokolov, B.V., and Yusupov, R.M., Intellektual’nye tekhnologii monitoringa i upravleniya strukturnoi dinamikoi slozhnykh tekhnicheskikh ob”ektov (Intellectual Technologies for Monitoring and Managing the Structural Dynamics of Complex Technical Objects), Moscow: Nauka, 2006.

    MATH  Google Scholar 

  25. Flach, P., Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge: University Press, 2012.

    Book  MATH  Google Scholar 

  26. Zaki, M. and Wagner, M., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge: University Press, 2014.

    Book  MATH  Google Scholar 

  27. Han, J., Data Mining. Concepts and Techniques, Waltham: Morgan Kaufmann, 2012.

    MATH  Google Scholar 

  28. Maslov, S., Teoriya deduktivnykh sistem i ee primenenie (Theory of Deductive Systems and Its Application), Moscow: Radio i svyaz’, 1986.

    MATH  Google Scholar 

  29. Tyugu, E. and Kharf, M., Algorithms of structural program synthesis, Programmirovanie, 1980, vol. 4, pp. 3–13.

    MATH  Google Scholar 

  30. Iskusstvennyi intellect (Artificial Intelligence), vol. 2: Modeli i metody. Spravochnik (Models and Methods. Handbook), Pospelov, D.A., Ed., Moscow: Nauka, 1990.

  31. Giacomo, G., Patrizi, F., and Sardina, S., Automatic behavior composition synthesis, Artif. Intell., 2013, vol. 196, pp. 106–142.

    Article  MathSciNet  MATH  Google Scholar 

  32. Kreitz, C., Program synthesis, in Automated Deduction—A Basis for Application, Kluwer Publ., 1998, vol. 10, pp. 105–134.

    Chapter  Google Scholar 

  33. Avellone, A., Ferrari, M., and Miglioli, P., Synthesis of programs in abstract data types., in Logic-Based Program Synthesis and Transformation (LOPSTR), Flener, P., Ed., 1998, vol. 1559, pp. 81–100. https://doi.org/www.link.springer.com/chapter/10.1007/3-540-48958-4_5#citeas.

    Article  Google Scholar 

  34. Robinson, J., A machine-oriented logic based on resolution principle, J. ACM, 1965, vol. 12, pp. 23–41.

    Article  MathSciNet  MATH  Google Scholar 

  35. Osipov, V.Yu., Avtomaticheskii sintez programm deistvii intellektual’nykh robotov. Programmirovanie (Automatic Synthesis of Action Programs of Intelligent Robots. Programming), Moscow: Nauka, 2016, no. 3, pp. 47–54.

    Google Scholar 

  36. Osipov, V.Yu., Synthesis of effective programs for managing information and computing resources, Prib. Sist. Upr, 1998, no. 12, pp. 24–27.

    Google Scholar 

  37. Kant, E., On the efficient synthesis of efficient programs, Artif. Intell, 1983, vol. 20, no. 3, pp. 253–305.

    Article  Google Scholar 

  38. Bibel, W., Korn, D., Kreitz, C., Kurucz, F., Otten, J., Schmitt, S., and Stolpmann, G., A multi-level approach to program synthesis, LOPSTR, 1998, pp. 1–27.

    Google Scholar 

  39. Fu, P. and Komendantskaya, E., A type—theoretic approach to resolution, LOPSTR, 2015, pp. 91–106.

    MATH  Google Scholar 

  40. Malkov, A. and Pershin, I., Sistemy s raspredelennymi parametrami. Analiz i sintez (Systems with Distributed Parameters. Analysis and Synthesis), Moscow: Nauchnyi mir, 2012.

    Google Scholar 

  41. Osipov, V., Lushnov, M., Stankova, E., and Vodyaho, A., Inductive synthesis of the models of biological systems according to clinical trials, International Conference on Computational Science and Its Applications (ICCSA 2017); Lect. Notes Comput. Sci., 2017, vol. 10404, pp. 103–115.

    Article  Google Scholar 

  42. Osipov, V., Vodyaho, A., Zhukova, N., and Glebovsky, P., Multilevel automatic synthesis of behavioral programs for smart devices, International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO 2017), IEEE, 2017, pp. 335–340.

  43. Osipov, V.Yu., Zhukova, N.A., Vodyaho, A.I., Kalmatsky, A., and Mustafin, N.G., Towards building of cable TV content-sensitive adaptive monitoring and management systems, Int. J. Comput. Commun., 2017, vol. 11, pp. 75–81.

    Google Scholar 

  44. Osipov, V., Vodyaho, A., and Zhukova, N., About one approach to multilevel behavioral program synthesis for television devices, Int. J. Comput. Commun., 2017, no. 11, pp. 17–25.

  45. Vitol, A.D., Deripaska, A.O., Zhukova, N.A., and Sokolov, I.S., Tekhnologiya adaptivnoi obrabotki izmeritel’nykh dannykh (Technology for Adaptive Processing of Measurement Data), St. Petersburg: Izd. S.-Peterb. Gos. Electrotekh. Univ. LETI, 2012.

    Google Scholar 

  46. Vodyakho, A.I. and Zhukova, N.A., Arkhitekturnyi pod-khod k postroeniyu adaptivnykh intellektual’nykh sistem analiza mnogomernykh izmerenii parametrov prostranstvenno sootnesennykh ob”ektov (An Architectural Approach to Constructing Adaptive Intelligent Systems for Analyzing Multidimensional Measurements of Parameters of Spatially Related Objects), St. Petersburg: Izd. S.-Peterb. Gos. Electrotekh. Univ. LETI, 2014.

    Google Scholar 

  47. Vasil’ev, A.V., Vaintraub, A.I., Vodyakho, A.I., Zhukova, N.A., Kurapeev, D.I., Lushnov, M.S., and Smirnov, A.V., Kognitivnye informatsionnye sistemy monitoringa (Cognitive Monitoring Information Systems), St. Petersburg: Izd. S.-Peterb. Gos. Electrotekh. Univ. LETI, 2018.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Russian Text © N.A. Zhukova, N.R. Andriyanova, 2019, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2019, No. 2, pp. 18–29.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhukova, N.A., Andriyanova, N.R. Cognitive Monitoring of Distributed Objects. Autom. Doc. Math. Linguist. 53, 32–43 (2019). https://doi.org/10.3103/S0005105519010084

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0005105519010084

Keywords

Navigation