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Toward Perception Based Computing: A Rough-Granular Perspective

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Web Intelligence Meets Brain Informatics (WImBI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4845))

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Abstract

We discus the Wisdom Granular Computing (WGC) as a basic methodology for Perception Based Computing (PBC). By wisdom, we understand an adaptive ability to make judgements correctly to a satisfactory degree (in particular, correct decisions) having in mind real-life constraints. We propose Rough-Granular Computing (RGC) as the basis for WGC.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C, pp. 207–216. ACM Press, New York (1993)

    Chapter  Google Scholar 

  2. Rough Set Exploration System (RSES). Available at: http://logic.mimuw.edu.pl/~rses

  3. Axelrod, R.M.: The Complexity of Cooperation. Princeton University Press, Princeton, NJ (1997)

    Google Scholar 

  4. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2003)

    MATH  Google Scholar 

  5. Bazan, J.: The Road simulator. Available at: http://logic.mimuw.edu.pl/~bazan/simulator

  6. Bazan, J., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.J.: Automatic planning of treatment of infants with respiratory failure through rough set modeling. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 418–427. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Bazan, J., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.J.: Risk pattern identification in the treatment of infants with respiratory failure through rough set modeling. In: Proceedings of IPMU 2006, Éditions E.D.K., Paris, July 2-7, 2006, pp. 2650–2657 (2006)

    Google Scholar 

  8. Bazan, J., Skowron, A., Swiniarski, R.: Rough sets and vague concept approximation: From sample approximation to adaptive learning. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 39–62. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Bazan, J.G., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślȩzak, et al. (eds.), pp. 688–697 [76]

    Google Scholar 

  10. Bazan, J.G., Skowron, A.: Classifiers based on approximate reasoning schemes. In: Dunin-Kȩplicz, et al. (eds.), pp. 191–202 [21]

    Google Scholar 

  11. Behnke, S.: Hierarchical Neural Networks for Image Interpretation. LNCS, vol. 2766. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  12. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, Oxford, UK (1999)

    MATH  Google Scholar 

  13. Breiman, L.: Statistical modeling: The two cultures. Statistical Science 16(3), 199–231 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  14. Brown, F.: Boolean Reasoning. Kluwer Academic Publishers, Dordrecht (1990)

    MATH  Google Scholar 

  15. Cassimatis, N.L.: A cognitive substrate for achievinbg human-level intelligence. AI Magazine 27, 45–56 (2006)

    Google Scholar 

  16. Cassimatis, N.L., Mueller, E.T., Winston, P.H.: Achieving human-level intelligence through integrated systems and research. AI Magazine 27, 12–14 (2006)

    Google Scholar 

  17. Desai, A.: Adaptive complex enterprices. Comm. ACM 48, 32–35 (2005)

    Article  Google Scholar 

  18. Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value function decomposition. Artificial Intelligence 13(5), 227–303 (2000)

    MATH  MathSciNet  Google Scholar 

  19. Doherty, P., Łukaszewicz, W., Skowron, A., Szałas, A.: Knowledge Representation Techniques: A Rough Set Approach. In: Studies in Fuzziness and Soft Computing 202, Springer, Heidelberg (2006)

    Google Scholar 

  20. Duda, R., Hart, P., Stork, R.: Pattern Classification. John Wiley & Sons, New York (2002)

    Google Scholar 

  21. Dunin-Kȩplicz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.): Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS’2004). Advances in Soft Computing. Springer, Heidelberg (2005)

    Google Scholar 

  22. Fahle, M., Poggio, T.: Perceptual Learning. MIT Press, Cambridge (2002)

    Google Scholar 

  23. Forbus, K.D., Hinrisch, T.R.: Companion congnitive systems: A step toward human-level ai. AI Magazine 27, 83–95 (2006)

    Google Scholar 

  24. Forbus, K.D., Hinrisch, T.R.: Engines of the brain: The computational instruction set of human cognition. AI Magazine 27, 15–31 (2006)

    Google Scholar 

  25. Frege, G.: Grundgesetzen der Arithmetik, 2. Verlag von Hermann Pohle, Jena (1903)

    Google Scholar 

  26. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  27. Gell-Mann, M.: The Quark and the Jaguar - Adventures in the Simple and the Complex. Brown and Co., London (1994)

    MATH  Google Scholar 

  28. Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier, Morgan Kaufmann, CA (2004)

    Google Scholar 

  29. Granger, R.: Engines of the brain: The computational instruction set of human cognition. AI Magazine 27(2), 15–31 (2006)

    Google Scholar 

  30. Halpern, J.Y., Fagin, R., Moses, Y., Vardi, M.Y.: Reasoning about Knowledge. MIT Press, Cambridge (1995)

    MATH  Google Scholar 

  31. Ivancevic, V.G., Ivancevic, T.T.: Geometrical Dynamics of Complex Systems. A Unified Modelling Approach to Physics, Control, Biomechanics, Neurodynamics and Psycho-Socio-Economical Dynamics. Springer, Dordrecht (2006)

    MATH  Google Scholar 

  32. Jankowski, A., Skowron, A.: A wistech paradigm for intelligent systems. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI: Journal Subline, vol. 4374, pp. 94–132. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  33. Jankowski, A., Skowron, A.: Logic for artificial intelligence: A Rasiowa–Pawlak school perspective. In: Ehrenfeucht, A., Marek, W., Srebrny, M. (eds.) Seventy Years of Fundational Studies, IOS Press, Amsterdam (2007)

    Google Scholar 

  34. Johnson, S.: Dictionary of the English Language in Which the Words are Deduced from Their Originals, and Illustrated in their Different Significations by Examples from the Best Writers, 2 volumes, F.C. and J. Rivington, London (1816)

    Google Scholar 

  35. Jones, R.M., Wray, R.E.: Comparative analysis of frameworks for knowledge-intensive intelligent agents. AI Magazine 27(2), 57–70 (2006)

    Google Scholar 

  36. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 227–303 (1996)

    Google Scholar 

  37. Kraus, S.: Strategic Negotiations in Multiagent Environments. MIT Press, Massachusetts (2001)

    Google Scholar 

  38. Langley, P.: Cognitive architectures and general intelligent systems. AI Magazine 27, 33–44 (2006)

    Google Scholar 

  39. Leibniz, G.W.: Dissertio de Arte Combinatoria, Leipzig (1666)

    Google Scholar 

  40. Leibniz, G.W.: New Essays on Human Understanding (1705) Translated and edited by Peter Remnant and Jonathan Bennett, Cambridge UP, Cambridge (1982)

    Google Scholar 

  41. Leśniewski, S.: Grungzüge eines neuen Systems der Grundlagen der Mathematik. Fundamenta Mathematicae 14, 1–81 (1929)

    Google Scholar 

  42. Liu, J.: Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization and Adaptive Computation. World Scientific Publishing, Singapore (2001)

    Google Scholar 

  43. Liu, J.: Web Intelligence (WI): What makes Wisdom Web? In: Proc. Eighteenth International Joint Conference on Artificial Intelligence (IJCAI 2003) pp. 1596–1601 (2003)

    Google Scholar 

  44. Liu, J., Jin, X., Tsui, K.C.: Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling. Kluwer/Springer, Heidelberg (2005)

    MATH  Google Scholar 

  45. Luck, M., McBurney, P., Preist, C.: Agent Technology. Enabling Next Generation Computing: A Roadmap for Agent Based Computing. AgentLink (2003)

    Google Scholar 

  46. Łukasiewicz, J.: Die logischen Grundlagen der Wahrscheinlichkeitsrechnung, Kraków1913. In: Borkowski, L. (ed.) Jan Łukasiewicz - Selected Works, pp. 16–63. North Holland & Polish Scientific Publishers, Amsterdam, London, Warsaw (1970)

    Google Scholar 

  47. McGovern, A.: Autonomous Discovery of Temporal Abstractions from Interaction with an Environment. PhD thesis, University of Massachusetts, Amherst (2002)

    Google Scholar 

  48. Miikkulainen, R., Bednar, J.A., Choe, Y., Sirosh, J.: Computational Maps in the Visual Cortex. Springer, Heidelberg (2005)

    Google Scholar 

  49. Mitchell, M.: A complex-systems perspective on the ”Computation vs. Dynamics” debate in cognitive science. In: Gernsbacher, M.A., Derry, S.J. (eds.) Proceedings of the 20th Annual Conference of the Cognitive Science Society (COGSCI 1998), pp. 710–715 (1998)

    Google Scholar 

  50. Mitchell, M., Newman, M.: Complex systems theory and evolution. In: Pagel, M. (ed.) Encyclopedia of Evolution, Oxford University Press, New York (2002)

    Google Scholar 

  51. Mitchell, M.: Complex systems: Network thinking. Artificial Intelligence 170(18), 1194–1212 (2006)

    Article  MathSciNet  Google Scholar 

  52. Nguyen, H.S.: Approximate boolean reasoning: Foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 344–523. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  53. Nguyen, H.S., Bazan, J., Skowron, A., Nguyen, S.H.: Layered learning for concept synthesis. In: Peters, J.F., Skowron, A., Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, Springer, Heidelberg (2004)

    Google Scholar 

  54. Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: Rough set approach to sunspot classification. In: Ślȩzak, et al. (eds.), pp. 263–272 [76]

    Google Scholar 

  55. Nguyen, T.T., Skowron, A.: Rough set approach to domain knowledge approximation. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 221–228. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  56. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  57. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving 9. Kluwer Academic Publishers, Dordrecht, The Netherlands (1991)

    MATH  Google Scholar 

  58. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  59. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177(1), 28–40 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  60. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Sciences 177(1), 41–73 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  61. Peters, J.F.: Approximation spaces for hierarchical intelligent behavioural system models. In: D.-Kepliçz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 13–30. Physica-Verlag, Heidelberg (2004)

    Google Scholar 

  62. Peters, J.F.: Rough ethology: Towards a biologically-inspired study of collective behaviour in intelligent systems with approximation spaces. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 153–174. Springer, Heidelberg (2005)

    Google Scholar 

  63. Peters, J.F., Henry, C.: Reinforcement learning with approximation spaces. Fundamenta Informaticae 71(2-3), 323–349 (2006)

    MATH  MathSciNet  Google Scholar 

  64. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)

    MATH  MathSciNet  Google Scholar 

  65. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15(4), 333–365 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  66. Rasiowa, H.: Algebraic models of logics. Warsaw University, Warsaw (2001)

    Google Scholar 

  67. Schlenoff, C., Albus, J., Messina, E., Barbera, A.J., Madhavan, R., Balakirsky, S.: Using 4d/rcs to address ai knowledge integration. AI Magazine 27, 71–81 (2006)

    Google Scholar 

  68. Segel, L.A., Cohen, I.R. (eds.): Design Principles for the Immune System and Other Distributed Autonomous Systems. Oxford University Press, New York (2001)

    Google Scholar 

  69. Skowron, A.: Rough sets in KDD (plenary talk). In: Shi, Z., Faltings, B., Musen, M. (eds.) 16-th World Computer Congress (IFIP 2000) Proceedings of Conference on Intelligent Information Processing (IIP 2000), Publishing House of Electronic Industry, Beijing, pp 1-14 (2000)

    Google Scholar 

  70. Skowron, A.: Perception logic in intelligent systems. In: Blair, S., et al. (eds.) Proceedings of the 8th Joint Conference on Information Sciences (JCIS 2005), Salt Lake City, Utah, USA, July 21-26, 2005, X-CD Technologies: A Conference & Management Company, Toronto, Ontario, Canada pp. 1–5 (2005)

    Google Scholar 

  71. Skowron, A.: Rough sets and vague concepts. Fundamenta Informaticae 64(1-4), 417–431 (2005)

    MATH  MathSciNet  Google Scholar 

  72. Skowron, A.: Rough sets in perception-based computing (keynote talk). In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 21–29. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  73. Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: Pal, et al. (eds.), [56] pp. 43–84.

    Google Scholar 

  74. Skowron, A., Stepaniuk, J., Peters, J.F., Swiniarski, R.: Calculi of approximation spaces. Fundamenta Informaticae 72(1-3), 363–378 (2006)

    MATH  MathSciNet  Google Scholar 

  75. Skowron, A., Stepaniuk, J.: Rough sets and granular computing: Toward rough-granular computing. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, Wiley, New York (in preparation, 2007)

    Google Scholar 

  76. Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.): RSFDGrC 2005. LNCS (LNAI), vol. 3642. Springer, Heidelberg (2005)

    Google Scholar 

  77. Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. MIT Press, Cambridge (2000)

    Google Scholar 

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

    Google Scholar 

  79. Swartout, W., Gratch, J., Hill, R.W., Hovy, E., Marsella, S., Rickel, J., Traum, D.: Towards virtual humans. AI Magazine (27), 96–108 (2006)

    Google Scholar 

  80. Sycara, K.: Multiagent systems. AI Magazine 19(2), 79–92 (1998)

    Google Scholar 

  81. Urmson, C., Anhalt, J., Clark, M., Galatali, T., Gonzalez, J.P., Gowdy, J., Gutierrez, A., Harbaugh, S., Johnson-Roberson, M., Kato, H., Koon, P.L., Peterson, K., Smith, B.K., Spiker, S., Tryzelaar, E., Whittaker, W.R.L.: High speed navigation of unrehearsed terrain: Red team technology for grand challenge 2004. Technical Report CMU-RI-TR-04-37, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (June 2004)

    Google Scholar 

  82. Van Wezel, W., Jorna, R., Meystel, A.: Planning in Intelligent Systems: Aspects, Motivations, and Methods. John Wiley & Sons, Hoboken, New Jersey (2006)

    MATH  Google Scholar 

  83. Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)

    MATH  Google Scholar 

  84. Yao, Y.Y., Zhong, N., Liu, J., Ohsuga, S.: Web Intelligence (WI): Research challenges and trends in the new information age. In: Zhong, N., Yao, Y., Ohsuga, S., Liu, J. (eds.) WI 2001. LNCS (LNAI), vol. 2198, pp. 1–17. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  85. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  86. Zadeh, L.A.: Outline of a new approach to the analysis of complex system and decision processes. IEEE Trans. on Systems, Man, and Cybernetics 3, 28–44 (1973)

    MATH  MathSciNet  Google Scholar 

  87. Zadeh, L.A.: Fuzzy sets and information granularity. In: Gupta, M., Ragade, R., Yager, R. (eds.) Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland, Amsterdam (1979)

    Google Scholar 

  88. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its certainty in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  89. 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, 105–119 (1999)

    MathSciNet  Google Scholar 

  90. Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)

    Google Scholar 

  91. Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU) - An outline. Information Sciences 171, 1–40 (2005)

    Article  MathSciNet  Google Scholar 

  92. Zhong, N., Liu, J., Yao, Y.Y.: Envisioning Intelligent Information Technologies (iIT) from the Stand-Point of Web Intelligence (WI). Communications of the ACM 50(3), 89–94 (2007)

    Article  Google Scholar 

  93. Zhong, N.: Impending Brain Informatics (BI) research from Web Intelligence (WI) perspective. International Journal of Information Technology and Decision Making 5(4), 713–727 (2006)

    Article  Google Scholar 

  94. Zhong, N., Liu, J. (eds.): Intelligent Technologies for Information Analysis. Springer, Berlin (2004)

    MATH  Google Scholar 

  95. Zhong, N., Liu, J., Yao, Y.Y. (eds.): Web Intelligence. Springer, Berlin (2003)

    MATH  Google Scholar 

  96. Zhong, N., Liu, J., Yao, Y.Y.: In search of the Wisdom Web. IEEE Computer 35(11), 27–31 (2002)

    Google Scholar 

  97. Zhong, N., Yao, Y.Y., Liu, J., Ohsuga, S.: Web Intelligence: Research and Development. In: Zhong, N., Yao, Y., Ohsuga, S., Liu, J. (eds.) WI 2001. LNCS (LNAI), vol. 2198, Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  98. Zhong, N., Liu, J., Yao, Y.Y., Ohsuga, S.: Web Intelligence (WI). In: Proc. the 24th IEEE Computer Society International Computer Software and Applications Conference (COMPSAC 2000), pp. 469–470. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Decision Processes Support, and AdgaM Solutions Sp. z o.o., Wa̧wozowa 9 lok. 64, 02-796 Warsaw, Poland

    Andrzej Jankowski

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

    Andrzej Skowron

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  1. Andrzej Jankowski
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Ning Zhong Jiming Liu Yiyu Yao Jinglong Wu Shengfu Lu Kuncheng Li

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Jankowski, A., Skowron, A. (2007). Toward Perception Based Computing: A Rough-Granular Perspective. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds) Web Intelligence Meets Brain Informatics. WImBI 2006. Lecture Notes in Computer Science(), vol 4845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77028-2_7

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