Skip to main content
SpringerLink
Log in

Towards W2T Foundations: Interactive Granular Computing and Adaptive Judgement

  • Chapter
  • First Online:
Wisdom Web of Things

Abstract

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.

[Hyper world] consists of the cyber, social, and physical worlds, [...] [Wisdom Web of Things] focuses on the data cycle, namely “from things to data, information, knowledge, wisdom, services, humans, and then back to things."A W2T data cycle system is designed to implement such a cycle, which is, technologically speaking, a practical way to realize the harmonious symbiosis of humans, computers, and things in the emerging hyper world.

– Ning Zhong et al. [53]

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Unlike most algorithms, they can run in environments unknown to the designer, and they learn by interacting with the environment how to act effectively in it. After sufficient interaction they will have expertise not provided by the designer, but extracted from the environment [46].

  2. 2.

    see, e.g., http://en.wikipedia.org/wiki/Vagueness, http://people.seas.harvard.edu/~valiant/researchinterests.htm, [46].

  3. 3.

    see http://en.wikipedia.org/wiki/Basel_Committee_on_Banking_Supervision.

  4. 4.

    see http://en.wikipedia.org/wiki/Basel_III.

References

  1. ISO 31000 standard, http://webstore.ansi.org/

  2. J. Barwise, J. Seligman, Information Flow: The Logic of Distributed Systems (Cambridge University Press, 1997)

    Google Scholar 

  3. J. Bazan, Hierarchical classifiers for complex spatio-temporal concepts. Trans. Rough Sets IX: J. Subline LNCS 5390, 474–750 (2008)

    Article  Google Scholar 

  4. D. Deutsch, A. Ekert, R. Lupacchini, Machines, logic and quantum physics. Bull. Symbol. Logic 6, 265–283 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  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. 189–208

    Chapter  Google Scholar 

  6. A. Einstein, Geometrie und Erfahrung (Geometry and Experience) (Julius Springer, Berlin, 1921)

    Book  MATH  Google Scholar 

  7. D. Goldin, S. Smolka, P. Wegner (eds.), Interactive Computation: The New Paradigm (Springer, 2006)

    Google Scholar 

  8. M. Heller, The Ontology of Physical Objects. Four Dimensional Hunks of Matter (Cambridge Studies in Philosophy, Cambridge University Press, 1990)

    Book  Google Scholar 

  9. A. Jankowski, Complex Systems Engineering: Wisdom for Saving Billions Based on Interactive Granular Computing (Springer, Heidelberg, 2016). (in preparation)

    Google Scholar 

  10. A. Jankowski, A. Skowron. A WisTech paradigm for intelligent systems. Trans. Rough Sets VI: J. Subline, 94–132

    Google Scholar 

  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. 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. A. Jankowski, A. Skowron, R.W. Swiniarski, Interactive complex granules. Fundamenta Informaticae 133, 181–196 (2014)

    MathSciNet  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  15. D. Kahneman, Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2002)

    Article  Google Scholar 

  16. L. Kari, G. Rozenberg, The many facets of natural computing. Commun. ACM 51, 72–83 (2008)

    Article  Google Scholar 

  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. P. Martin-Löf, Intuitionistic Type Theory (Notes by Giovanni Sambin of a series of lectures given in Padua, June 1980) (Bibliopolis, Napoli, Italy, 1984)

    MATH  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  21. A. Omicini, A. Ricci, M. Viroli. The multidisciplinary patterns of interaction from sciences to computer science, in Goldin et al. [7], pp. 395–414

    Google Scholar 

  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. Z. Pawlak, A. Skowron, Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Z. Pawlak, Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Book  Google Scholar 

  26. J. Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving (The Addison Wesley, Moston, MA, 1984)

    Google Scholar 

  27. J. Pearl, Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  28. W. Pedrycz, S. Skowron, V. Kreinovich (eds.), Handbook of Granular Computing (Wiley, Hoboken, NJ, 2008)

    Google Scholar 

  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. 774–781

    Google Scholar 

  30. L. Polkowski, A. Skowron, Rough mereology: a new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15(4), 333–365 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    MathSciNet  Google Scholar 

  32. I. Rahwan, G.R. Simari, Argumentation in Artificial Intelligence (Springer, Berlin, 2009)

    Google Scholar 

  33. G. Rozenberg, T. Bäck, J. Kok (eds.), Handbook of Natural Computing (Springer, 2012)

    Google Scholar 

  34. L. Schäfers, Parallel Monte-Carlo Tree Search for HPC Systems and its Application to Computer Go (Logos Verlag, Berlin, 2014)

    Google Scholar 

  35. P. Shevchenko (ed.), Modelling Operational Risk Using Bayesian Inference (Springer, 2011)

    Google Scholar 

  36. A. Skowron, A. Jankowski, P. Wasilewski, Risk management and interactive computational systems. J. Adv. Math. Appl. 1, 61–73 (2012)

    Google Scholar 

  37. A. Skowron, J. Stepaniuk. Information granules and rough-neural computing, in Pal et al. [22], pp. 43–84

    Google Scholar 

  38. A. Skowron, J. Stepaniuk, R. Swiniarski, Modeling rough granular computing based on approximation spaces. Inf. Sci. 184, 20–43 (2012)

    Article  MATH  Google Scholar 

  39. A. Skowron, P. Wasilewski, Information systems in modeling interactive computations on granules. Theor. Comput. Sci. 412(42), 5939–5959 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  40. A. Skowron, P. Wasilewski, Interactive information systems: toward perception based computing. Theor. Comput. Sci. 454, 240–260 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  41. P. Slovik, Cournède: Macroeconomic Impact of Basel III, Working Papers, vol. 844 (OECD Economics Publishing, OECD Economics Department, 2011) http://www.oecd.org/eco/Workingpapers

  42. J. Stepaniuk, Rough-Granular Computing in Knowledge Discovery and Data Mining (Springer, Heidelberg, 2008)

    MATH  Google Scholar 

  43. R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (The MIT Press, 1998)

    Google Scholar 

  44. L.P. Thiele, The Heart of Judgment: Practical Wisdom, Neuroscience, and Narrative (Cambridge University Press, Cambridge, UK, 2010)

    Google Scholar 

  45. V. Vapnik, Statistical Learning Theory (Wiley, New York, NY, 1998)

    MATH  Google Scholar 

  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. A. Zadeh, Computing with Words: Principal Concepts and Ideas, Studies in Fuzziness and Soft Computing, vol. 277 (Springer, Heidelberg, 2012)

    Book  MATH  Google Scholar 

  48. L.A. Zadeh, Fuzzy sets and information granularity, in Advances in Fuzzy Set Theory and Applications (North-Holland, Amsterdam, 1979), pp. 3–18

    Google Scholar 

  49. L.A. Zadeh, Fuzzy Logic = Computing With Words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  51. L.A. Zadeh, Foreword, in Pal et al. [22], pp. IX–XI

    Google Scholar 

  52. L.A. Zadeh, A new direction in AI: toward a computational theory of perceptions. AI Mag. 22(1), 73–84 (2001)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  2. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Andrzej Skowron

  3. The Dziubanski Foundation of Knowledge Technology, Nowogrodzka 31, 00-511, Warsaw, Poland

    Andrzej Jankowski

Authors
  1. Andrzej Skowron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Jankowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrzej Skowron .

Editor information

Editors and Affiliations

  1. Dep of Life Sci & Informatic, Maebashi Institute of Technology Dep of Life Sci & Informatic, Maebashi, Japan

    Ning Zhong

  2. Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan

    Jianhua Ma

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  4. Faculty of Computer and Information Science, Hosei University , Tokyo, Japan

    Runhe Huang

  5. Department of Mathematics and Computing, The University of Southern Queensland, Toowoomba, Queensland, Australia

    Xiaohui Tao

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Skowron, A., Jankowski, A. (2016). Towards W2T Foundations: Interactive Granular Computing and Adaptive Judgement. In: Zhong, N., Ma, J., Liu, J., Huang, R., Tao, X. (eds) Wisdom Web of Things. Web Information Systems Engineering and Internet Technologies Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-44198-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44198-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44196-2

  • Online ISBN: 978-3-319-44198-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation