Towards W2T Foundations: Interactive Granular Computing and Adaptive Judgement

  • Andrzej SkowronEmail author
  • Andrzej Jankowski
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)


Development of methods for Wisdom Web of Things (W2T) should be based on foundations of computations performed in the complex environments of W2T. We discuss some characteristic features of decision making processes over W2T. First of all the decisions in W2T are very often made by different agents on the basis of complex vague concepts and relations among them (creating domain ontology) which are semantically far away from dynamically changing and huge raw data. Methods for approximation of such vague concepts based on information granulation are needed. It is also important to note that the abstract objects represented by different agents are dynamically linked by them with some physical objects and the aim is very often to control performance of computations in the physical world for achieving the target goals. Moreover, the decision making by different agents working in the W2T environment requires mechanisms for understanding (to a satisfactory degree) reasoning performed in natural language on concepts and relations from the domain ontology. We discuss a new computation model, where computations are progressing due to interactions of complex granules (c-granules) linked with the physical objects. C-granules are defined relative to a given agent. We extend the existing Granular Computing (GrC) approach by introducing complex granules (c-granules, for short) making it possible to model interactive computations of agents in complex systems over W2T. One of the challenges in the approach is to develop methods and strategies for adaptive reasoning, called adaptive judgement, e.g., for adaptive control of computations. In particular, adaptive judgement is required in the risk/efficiency management by agents supported by W2T. The discussed approach is a step toward realization of the Wisdom Technology (WisTech) program. The approach was developed over years of work on different real-life projects.


Risk Management Physical World Domain Ontology Information Granule Vague Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by the Polish National Science Centre (NCN) grants DEC-2011/01/D/ST6/06981, DEC-2013/09/B/ST6/01568 as well as by the Polish National Centre for Research and Development (NCBiR) under the grant DZP/RID-I-44/8/NCBR/2016.


  1. 1.
    ISO 31000 standard,
  2. 2.
    J. Barwise, J. Seligman, Information Flow: The Logic of Distributed Systems (Cambridge University Press, 1997)Google Scholar
  3. 3.
    J. Bazan, Hierarchical classifiers for complex spatio-temporal concepts. Trans. Rough Sets IX: J. Subline LNCS 5390, 474–750 (2008)CrossRefGoogle Scholar
  4. 4.
    D. Deutsch, A. Ekert, R. Lupacchini, Machines, logic and quantum physics. Bull. Symbol. Logic 6, 265–283 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    A. Ehrenfeucht, J. Kleijn, M. Koutny, G. Rozenberg, Reaction systems: a natural computing approach to the functioning of living cells, in A Computable Universe, Understanding and Exploring Nature as Computation, ed. by H. Zenil (World Scientific, Singapore, 2012), pp. 189–208CrossRefGoogle Scholar
  6. 6.
    A. Einstein, Geometrie und Erfahrung (Geometry and Experience) (Julius Springer, Berlin, 1921)CrossRefzbMATHGoogle Scholar
  7. 7.
    D. Goldin, S. Smolka, P. Wegner (eds.), Interactive Computation: The New Paradigm (Springer, 2006)Google Scholar
  8. 8.
    M. Heller, The Ontology of Physical Objects. Four Dimensional Hunks of Matter (Cambridge Studies in Philosophy, Cambridge University Press, 1990)CrossRefGoogle Scholar
  9. 9.
    A. Jankowski, Complex Systems Engineering: Wisdom for Saving Billions Based on Interactive Granular Computing (Springer, Heidelberg, 2016). (in preparation)Google Scholar
  10. 10.
    A. Jankowski, A. Skowron. A WisTech paradigm for intelligent systems. Trans. Rough Sets VI: J. Subline, 94–132Google Scholar
  11. 11.
    A. Jankowski, A. Skowron. Wisdom technology: a rough-granular approach, in M. Marciniak, A. Mykowiecka (eds.), Bolc Festschrift, Lectures Notes in Computer Science, vol. 5070, pp. 3–41 (Springer, Heidelberg, 2009)Google Scholar
  12. 12.
    A. Jankowski, A. Skowron, R.W. Swiniarski. Interactive computations: toward risk management in interactive intelligent systems, in P. Maji, A. Ghosh, M.N. Murty, K. Ghosh, S.K. Pal (eds.), Pattern Recognition and Machine Intelligence—5th International Conference, PReMI 2013, Kolkata, India, December 10–14, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8251, pp. 1–12 (Springer, 2013)Google Scholar
  13. 13.
    A. Jankowski, A. Skowron, R.W. Swiniarski, Interactive complex granules. Fundamenta Informaticae 133, 181–196 (2014)MathSciNetGoogle Scholar
  14. 14.
    A. Jankowski, A. Skowron, R.W. Swiniarski, Perspectives on uncertainty and risk in rough sets and interactive rough-granular computing. Fundamenta Informaticae 129, 69–84 (2014)MathSciNetzbMATHGoogle Scholar
  15. 15.
    D. Kahneman, Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2002)CrossRefGoogle Scholar
  16. 16.
    L. Kari, G. Rozenberg, The many facets of natural computing. Commun. ACM 51, 72–83 (2008)CrossRefGoogle Scholar
  17. 17.
    F. Lamnabhi-Lagarrigue, M.D. Di Benedetto, E. Schoitsch, Introduction to the special theme cyber-physical systems. Ercim News 94, 6–7 (2014)Google Scholar
  18. 18.
    P. Martin-Löf, Intuitionistic Type Theory (Notes by Giovanni Sambin of a series of lectures given in Padua, June 1980) (Bibliopolis, Napoli, Italy, 1984)zbMATHGoogle Scholar
  19. 19.
    J.M. Mendel, L.A. Zadeh, E. Trillas, R.Yager, J. Lawry, H. Hagras, S. Guadarrama, What computing with words means to me. IEEE Comput. Intell. Mag. pp. 20–26 (February 2010)Google Scholar
  20. 20.
    S.H. Nguyen, J. Bazan, A. Skowron, H.S. Nguyen, Layered learning for concept synthesis. Trans. Rough Sets I: J. Subline LNCS 3100, 187–208 (2004)CrossRefzbMATHGoogle Scholar
  21. 21.
    A. Omicini, A. Ricci, M. Viroli. The multidisciplinary patterns of interaction from sciences to computer science, in Goldin et al. [7], pp. 395–414Google Scholar
  22. 22.
    S.K. Pal, L. Polkowski, A. Skowron (eds.), Rough-Neural Computing: Techniques for Computing with Words (Cognitive Technologies, Springer, Heidelberg, 2004)Google Scholar
  23. 23.
    Z. Pawlak, A. Skowron, Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Z. Pawlak, Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9 (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991)CrossRefGoogle Scholar
  26. 26.
    J. Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving (The Addison Wesley, Moston, MA, 1984)Google Scholar
  27. 27.
    J. Pearl, Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    W. Pedrycz, S. Skowron, V. Kreinovich (eds.), Handbook of Granular Computing (Wiley, Hoboken, NJ, 2008)Google Scholar
  29. 29.
    L. Polkowski, A. Skowron, Rough mereological approach to knowledge-based distributed ai, in J.K. Lee, J. Liebowitz, J.M. Chae (eds.), Critical Technology, Proc, Third World Congress on Expert Systems, February 5–9, Soeul, Korea (Cognizant Communication Corporation, New York, 1996), pp. 774–781Google Scholar
  30. 30.
    L. Polkowski, A. Skowron, Rough mereology: a new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15(4), 333–365 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    L. Polkowski, A. Skowron, Rough mereological calculi of granules: a rough set approach to computation. Comput. Intell. Int. J. 17(3), 472–492 (2001)MathSciNetGoogle Scholar
  32. 32.
    I. Rahwan, G.R. Simari, Argumentation in Artificial Intelligence (Springer, Berlin, 2009)Google Scholar
  33. 33.
    G. Rozenberg, T. Bäck, J. Kok (eds.), Handbook of Natural Computing (Springer, 2012)Google Scholar
  34. 34.
    L. Schäfers, Parallel Monte-Carlo Tree Search for HPC Systems and its Application to Computer Go (Logos Verlag, Berlin, 2014)Google Scholar
  35. 35.
    P. Shevchenko (ed.), Modelling Operational Risk Using Bayesian Inference (Springer, 2011)Google Scholar
  36. 36.
    A. Skowron, A. Jankowski, P. Wasilewski, Risk management and interactive computational systems. J. Adv. Math. Appl. 1, 61–73 (2012)Google Scholar
  37. 37.
    A. Skowron, J. Stepaniuk. Information granules and rough-neural computing, in Pal et al. [22], pp. 43–84Google Scholar
  38. 38.
    A. Skowron, J. Stepaniuk, R. Swiniarski, Modeling rough granular computing based on approximation spaces. Inf. Sci. 184, 20–43 (2012)CrossRefzbMATHGoogle Scholar
  39. 39.
    A. Skowron, P. Wasilewski, Information systems in modeling interactive computations on granules. Theor. Comput. Sci. 412(42), 5939–5959 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    A. Skowron, P. Wasilewski, Interactive information systems: toward perception based computing. Theor. Comput. Sci. 454, 240–260 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    P. Slovik, Cournède: Macroeconomic Impact of Basel III, Working Papers, vol. 844 (OECD Economics Publishing, OECD Economics Department, 2011)
  42. 42.
    J. Stepaniuk, Rough-Granular Computing in Knowledge Discovery and Data Mining (Springer, Heidelberg, 2008)zbMATHGoogle Scholar
  43. 43.
    R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (The MIT Press, 1998)Google Scholar
  44. 44.
    L.P. Thiele, The Heart of Judgment: Practical Wisdom, Neuroscience, and Narrative (Cambridge University Press, Cambridge, UK, 2010)Google Scholar
  45. 45.
    V. Vapnik, Statistical Learning Theory (Wiley, New York, NY, 1998)zbMATHGoogle Scholar
  46. 46.
    L. Valiant, Probably Approximately Correct. Nature’s Algorithms for Learning and Prospering in a Complex World (Basic Books, A Member of the Perseus Books Group, New York, 2013)Google Scholar
  47. 47.
    A. Zadeh, Computing with Words: Principal Concepts and Ideas, Studies in Fuzziness and Soft Computing, vol. 277 (Springer, Heidelberg, 2012)CrossRefzbMATHGoogle Scholar
  48. 48.
    L.A. Zadeh, Fuzzy sets and information granularity, in Advances in Fuzzy Set Theory and Applications (North-Holland, Amsterdam, 1979), pp. 3–18Google Scholar
  49. 49.
    L.A. Zadeh, Fuzzy Logic = Computing With Words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)CrossRefGoogle Scholar
  50. 50.
    L.A. Zadeh, From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Trans. Circuits Syst. 45, 105–119 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  51. 51.
    L.A. Zadeh, Foreword, in Pal et al. [22], pp. IX–XIGoogle Scholar
  52. 52.
    L.A. Zadeh, A new direction in AI: toward a computational theory of perceptions. AI Mag. 22(1), 73–84 (2001)zbMATHGoogle Scholar
  53. 53.
    N. Zhong, J.H. Ma, R.H. Huang, J.M. Liu, Y.Y. Yao, Y.X. Zhang, J. Chen, Research challenges and perspectives on Wisdom Web of Things (W2T). J. Supercomput. 64, 862–882 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  3. 3.The Dziubanski Foundation of Knowledge TechnologyWarsawPoland

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