Toward Computer-Aided Interpretation of Situations

  • Juliusz L. KulikowskiEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)


The problem of interpretation of situations as a widely extended and important component of living beings’ behavior in real world is considered. A scheme of interpretation of situations in natural living beings is presented and a general scheme of inspired by the nature artificial situations interpreting system is proposed. Basic constraints imposed on computer-based situations interpreting systems are described. It is shown that the computer-based situations interpreting systems are an extension of pattern recognition systems and the differences between them are characterized. The role of domain ontologies and of ontological models in computer-based situations interpreting systems design is shown and it is illustrated by examples.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley Publishing Co., Redwood City (1991)Google Scholar
  2. 2.
    Levy, B.C., Adams, M.B.: Global optimization with stochastic neural networks. In: IEEE Conf. on Neural Networks, San Diego, USA, pp. 681–689 (1987)Google Scholar
  3. 3.
    Hopfield, J.J.: Artificial neural networks. IEEE Circuits and Devices Magazine, 3–10 (1988)Google Scholar
  4. 4.
    Rilling, J.K., Insel, T.R.: The primate neocortex in comparative perspective using magnetic resonance imaging. Journal of Human Evolution 37, 191–223 (1999)CrossRefGoogle Scholar
  5. 5.
    Allen, J.S.: The lives of the brain. The Harvard College (2009)Google Scholar
  6. 6.
    Rademacher, J., Galaburda, A.M., Kennedy, D.N., et al.: Human cerebral cortex: Localization, parcellation and morphometry with magnetic resonance imaging. Journal of Cognitive Neuroscience 4, 352–374 (1992)CrossRefGoogle Scholar
  7. 7.
    Ramnani, N., Behrens, T.E.J., Johansen-Berg, H., et al.: The evolution of prefrontal inputs to the corticopontine system: Diffusion imaging evidence from macaque monkeys and humans. Cerebral Cortex 16, 811–818 (2006)CrossRefGoogle Scholar
  8. 8.
    Fernandez-Lopez, M., Gomez-Perez, A.: Overview and analysis of methodologies for building ontologies. The Knowledge Eng. Rev. 17, 129–156 (2002)Google Scholar
  9. 9.
    Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  10. 10.
    Kulikowski, J.L.: The Role of ontological models in pattern recognition. In: Kurzynski, M., et al. (eds.) Computer Recognition Systems. Advances in Soft Computing, pp. 43–52. Springer, Berlin (2005)CrossRefGoogle Scholar
  11. 11.
    Kulikowski, J.L.: Relational approach to structural analysis of images. Machine Graphics and Vision 1, 299–309 (1992)Google Scholar
  12. 12.
    Kulikowski, J.L.: Pattern recognition based on ambiguous indications of experts. In: Kurzynski, M. (ed.) Komputerowe Systemy Rozpoznawania, KOSYR 2001, Wroclaw, pp. 15–22 (2001)Google Scholar
  13. 13.
    Owsinski, J.W., Brüggemann, R. (eds.): Multicriteria ordering and ranking: partial orders, ambiguities and applied issues. System Research Institute, Warsaw (2008)Google Scholar
  14. 14.
    Duda, R., Hart, O., Stork, D.: Pattern classification. John Wiley, New York (2000)Google Scholar
  15. 15.
    Przytulska, M., Kulikowski, J.L., et al.: Computer methods of radiological images analysis for pathomorphological lesions assessment in selected inner organs. Report on the project N N518 4211 33 IBBE PAS Warsaw (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Polish Academy of SciencesNalecz Institute of Biocybernetics and Biomedical EngineeringWarsawPoland

Personalised recommendations