Advertisement

An Introduction to Perception Based Computing

  • Andrzej Skowron
  • Piotr Wasilewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6485)

Abstract

We discuss basic notions of Perception Based Computing (PBC). Perception is characterized by sensory measurements and ability to apply them to reason about satisfiability of complex vague concepts used, e.g., as guards for actions or invariants to be preserved by agents. Such reasoning is often referred as adaptive judgment. Vague concepts can be approximated on the basis of sensory attributes rather than defined exactly. Approximations usually need to be induced by using hierarchical modeling. Computations require interactions between granules of different complexity, such as elementary sensory granules, granules representing components of agent states, or complex granules representing classifiers that approximate concepts. We base our approach to interactive computations on generalized information systems and rough sets. We show that such systems can be used for modeling advanced forms of interactions in hierarchical modeling. Unfortunately, discovery of structures for hierarchical modeling is still a challenge. On the other hand, it is often possible to acquire or approximate them from domain knowledge. Given appropriate hierarchical structures, it becomes feasible to perform adaptive judgment, starting from sensory measurements and ending with conclusions about satisfiability degrees of vague target guards. Thus, our main claim is that PBC should enable users (experts, researchers, students) to submit domain knowledge, by means of a dialog. It should be also possible to submit hypotheses about domain knowledge to be checked semi-automatically. PBC should be designed more like laboratories helping users in their research rather than fully automatic data mining or knowledge discovery toolkit. In particular, further progress in understanding visual perception – as a special area of PBC – will be possible, if it becomes more open for cooperation with experts from neuroscience, psychology or cognitive science. In general, we believe that PBC will soon become necessity in many research areas.

Keywords

Rough sets granular computing interactive computations perception based computing information systems perception attributes sensory attributes action attributes approximation of complex concepts ontology approximation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arbib, M.A.: The Metaphorical Brain 2: Neural Networks and Beyond. Willey & Sons, Chichester (1989)zbMATHGoogle Scholar
  2. 2.
    Bara, B.G.: Cognitive Science. A Developmental Approach to the Simulation of the Mind. Lawrence Erlbaum Associates, Hove (1995)Google Scholar
  3. 3.
    Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. Cambridge University Press, Cambridge (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    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
  5. 5.
    Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT Press, Cambridge (2001)Google Scholar
  6. 6.
    Chakraborty, M.K., Pagliani, P.: Geometry Of Approximation: Rough Set Theory: Logic, Algebra and Topology of Conceptual Patterns. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  7. 7.
    Goldin, D., Smolka, S., Wegner, P. (eds.): Interactive Computation: The New Paradigm. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  8. 8.
    Jankowski, J., Skowron, A.: Wisdom technology: A Rough-granular approach. In: Marciniak, M., Mykowiecka, A. (eds.) Bolc Festschrift. LNCS, vol. 5070, pp. 3–41. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Khan, M.A., Banerjee, M.: A study of multiple-source approximation systems. In: Peters, J.F., Skowron, A., Słowiński, R., Lingras, P., Miao, D., Tsumoto, S. (eds.) Rough Sets XII. LNCS, vol. 6190, pp. 46–75. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Maar, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, New York (1982)Google Scholar
  11. 11.
    Mendel, J.M., Wu, D.: Perceptual Computing: Aiding People in Making Subjective Judgments. John Wiley & IEEE Press (2010)Google Scholar
  12. 12.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  13. 13.
    Pawlak, Z.: Rough sets. International Journal of Computing and Information Sciences 18, 341–356 (1982)CrossRefzbMATHGoogle Scholar
  14. 14.
    Pawlak, Z.: Rough sets. In: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)Google Scholar
  15. 15.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Science 177, 3–27 (2007); Rough sets: Some extensions. Information Science 177, 28–40 (2007); Rough sets and boolean reasoning. Information Science 177, 41–73 (2007) Google Scholar
  16. 16.
    Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, Chichester (2008)Google Scholar
  17. 17.
    Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Skowron, A., Stepaniuk, J.: Informational granules and rough-neural computing. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words, pp. 43–84. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Skowron, A., Stepaniuk, J.: Approximation spaces in rough-granular computing. Fundamenta Informaticae 100, 141–157 (2010)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Skowron, A., Stepaniuk, J.: Rough granular computing based on approximation spaces (Extended version of [19] submitted to the special issue of Theoretical Computer Science on Rough-Fuzzy Computing)Google Scholar
  21. 21.
    Skowron, A., Suraj, Z.: Discovery of concurrent data models from experimental tables: A rough set approach. In: Proceedings of First International Conference on Knowledge Discovery and Data Mining, pp. 288–293. AAAI Press, Menlo Park (1995)Google Scholar
  22. 22.
    Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. In: Szczuka, M. (ed.) RSCTC 2010. LNCS, vol. 6086, pp. 730–739. Springer, Heidelberg (2010)Google Scholar
  23. 23.
    Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules (Extended version of [22] submitted to the special issue of Theoretical Computer Science on Rough-Fuzzy Computing)Google Scholar
  24. 24.
    Ślęzak, D., Toppin, G.: Injecting domain knowledge into a granular database engine – A position paper. In: CIKM 2010, Toronto, Ontario, Canada, October 26-30 (2010)Google Scholar
  25. 25.
    Sun, R.: Prolegomena to Integrating cognitive modeling and social simulation. In: Sun, R. (ed.) From Cognitive Modeling to Social Simulation, pp. 3–26. Cambridge University Press, Cambridge (2006)Google Scholar
  26. 26.
    Taatgen, N., Lebiere, C., Anderson, J.: Modeling paradigms in ACT-R 29. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation, pp. 29–52. Cambridge University Press, Cambridge (2006)Google Scholar
  27. 27.
    Thagard, P.: Mind: Introduction to Cognitive Science, 2nd edn. MIT Press, Cambridge (2005)Google Scholar
  28. 28.
    Zadeh, L.A.: Computing with words and perceptions – A paradigm shift. In: Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI 2009), Las Vegas, Nevada, USA, IEEE Systems, Man, and Cybernetics Society (2009)Google Scholar
  29. 29.
    Zadeh, L.A.: Generalized theory of uncertainty (GTU) – principal concepts and ideas. Computational Statistics & Data Analysis 51(1), 15–46 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Zadeh, L.A.: Precisiated natural language (PNL). AI Magazine 25(3), 74–91 (2004)Google Scholar
  31. 31.
    Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)zbMATHGoogle Scholar
  32. 32.
    Zadeh, L.A.: From computing with numbers to computing with words – From manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems 45(1), 105–119 (1999)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrzej Skowron
    • 1
  • Piotr Wasilewski
    • 1
  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland

Personalised recommendations