Interactive Rough-Granular Computing in Wisdom Technology

  • Andrzej Jankowski
  • Andrzej Skowron
  • Roman Swiniarski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)


Understanding of interactions is the critical issue of complex systems. Interactions in physical world are represented by information granules. We propose to model complex systems by interactive intelligent systems (IIS) created by societies of agents. Computations in IIS are based on complex granules (c-granules, for short). Adaptive judgement allows us to reason about c-granules and interactive computations performed on them. In adaptive judgement, different kinds of reasoning are involved such as deduction, induction, abduction or reasoning by analogy as well as intuitive judgement. In modeling of mental parts of c-granules, called information granules (infogranules, for short), we use the approach based on the rough set methods in combination with other soft computing approaches. Issues related to interactions among objects in the physical and mental worlds as well as adaptive judgement belong to the fundamental issues in Wisdom Technology (WisTech). In the paper we concentrate on some basic issues related to interactive computations over c-granules. WisTech was developed over years of work on different real-life projects. It can also be treated as a basis in searching for solutions of problems in such areas as Active Media Technology and Wisdom Web of Things.


granular computing rough sets interactions information granule physical object complex granule interactive intelligent system active media technology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bazan, J.: Hierarchical classifiers for complex spatio-temporal concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Bohm, D., Peat, F.D.: Science, Order, and Creativity, pp. 240–247. Bantam, New York (1987)Google Scholar
  3. 3.
    Goldin, D., Smolka, S., Wegner, P. (eds.): Interactive Computation: The New Paradigm. Springer (2006)Google Scholar
  4. 4.
    Gurevich, Y.: Interactive algorithms 2005. In: Goldin, et al. (eds.) [3], pp. 165–181Google Scholar
  5. 5.
    Heller, M.: The Ontology of Physical Objects. Four Dimensional Hunks of Matter. Cambridge Studies in Philosophy. Cambridge University Press (1990)Google Scholar
  6. 6.
    Jankowski, A., Skowron, A.: A WisTech paradigm for intelligent systems. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J.W., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 94–132. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Jankowski, A., Skowron, A.: Practical Issues of Complex Systems Engineering: Wisdom Technology Approach. Springer, Heidelberg (2013)Google Scholar
  8. 8.
    Kahneman, D.: Maps of bounded rationality: Psychology for behavioral economics. The American Economic Review 93, 1449–1475 (2002)CrossRefGoogle Scholar
  9. 9.
    Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)Google Scholar
  10. 10.
    Liu, J.: Active media technologies (AMT) from the standpoint of the Wisdom Web. In: Li, Y., Looi, M., Zhong, N. (eds.) Advances in Intelligent IT - Active Media Technology 2006, Proceedings of the 4th International Conference on Active Media Technology, AMT 2006, Brisbane, Australia, June 7-9. Frontiers in Artificial Intelligence and Applications, vol. 138. IOS Press, Brisbane (2006)Google Scholar
  11. 11.
    Marsh, L.: Stigmergic epistemology, stigmergic cognition. Journal Cognitive Systems 9, 136–149 (2008)CrossRefGoogle Scholar
  12. 12.
    Noë, A.: Action in Perception. MIT Press (2004)Google Scholar
  13. 13.
    Omicini, A., Ricci, A., Viroli, M.: The multidisciplinary patterns of interaction from sciences to computer science. In: Goldin, et al. (eds.) [3], pp. 395–414Google Scholar
  14. 14.
    Pawlak, Z.: Information systems - theoretical foundations. Information Systems 6, 205–218 (1981)CrossRefzbMATHGoogle Scholar
  15. 15.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)CrossRefGoogle Scholar
  16. 16.
    Pedrycz, W., Skowron, S., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, Hoboken (2008)CrossRefGoogle Scholar
  17. 17.
    Skowron, A., Stepaniuk, J.: Hierarchical modelling in searching for complex patterns: Constrained sums of information systems. Journal of Experimental and Theoretical Artificial Intelligence 17, 83–102 (2005)CrossRefzbMATHGoogle Scholar
  18. 18.
    Skowron, A., Stepaniuk, J., Jankowski, A., Bazan, J.G., Swiniarski, R.: Rough set based reasoning about changes. Fundamenta Informaticae 119(3-4), 421–437 (2012)MathSciNetGoogle Scholar
  19. 19.
    Skowron, A., Stepaniuk, J., Swiniarski, R.: Modeling rough granular computing based on approximation spaces. Information Sciences 184, 20–43 (2012)CrossRefGoogle Scholar
  20. 20.
    Skowron, A., Suraj, Z. (eds.): Rough Sets and Intelligent Systems. Professor Zdzislaw Pawlak in Memoriam. Series Intelligent Systems Reference Library. Springer (2013)Google Scholar
  21. 21.
    Skowron, A., Szczuka, M.: Toward interactive computations: A rough-granular approach. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning II. SCI, vol. 263, pp. 23–42. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. Theoretical Computer Science 412(42), 5939–5959 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Skowron, A., Wasilewski, P.: Interactive information systems: Toward perception based computing. Theoretical Computer Science 454, 240–260 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)zbMATHGoogle Scholar
  25. 25.
    Zadeh, L.A.: Fuzzy sets and information granularity. In: Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland, Amsterdam (1979)Google Scholar
  26. 26.
    Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)Google Scholar
  28. 28.
    Zhong, N., Ma, J.H., Huang, R., Liu, J., Yao, Y., Zhang, Y.X., Chen, J.: Research challenges and perspectives on Wisdom Web of Things (W2T). The Journal of Supercomputing 64, 862–882 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Andrzej Jankowski
    • 1
  • Andrzej Skowron
    • 2
  • Roman Swiniarski
    • 3
    • 4
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland
  2. 2.Institute of MathematicsWarsaw UniversityWarsawPoland
  3. 3.Department of Computer ScienceSan Diego State UniversitySan DiegoUSA
  4. 4.Institute of Computer Science Polish Academy of SciencesWarsawPoland

Personalised recommendations