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Wisdom Technology: A Rough-Granular Approach

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Aspects of Natural Language Processing

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5070))

Abstract

We discuss foundations for modern intelligent systems in the framework of Wisdom Technology (Wistech). The approach is based on the rough-granular approach.

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References

  1. Axaelrod, R.M.: The Complexity of Cooperation. Princeton University Press, Princeton (1997)

    Google Scholar 

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

    Book  MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  4. Bazan, J., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 688–697. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  5. Bazan, J., Skowron, A.: On-line elimination of non-relevant parts of complex objects in behavioral pattern identification. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 720–725. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Bazan, J., Skowron, A.: Classifiers based on approximate reasoning schemes. In: [16], pp. 191–202. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Bazan, J.G., Skowron, A., Świniarski, R.W.: 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 

  8. 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, Paris, France, July 2–7, 2006, pp. 2650–2657. Éditions E.D.K, Paris (2006)

    Google Scholar 

  9. 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 

  10. Bazan, J.: Rough sets and granular computing in behavioral pattern identification and planning. In: [62], pp. 777–822 (2007)

    Google Scholar 

  11. Birkhoff, G.: Lattice Theory, 3rd edn. AMS Colloquium Publications, vol. 25. American Mathematical Society, Providence (1967)

    MATH  Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  14. Desai, A.: Adaptive complex enterprises. Communications ACM 48(5), 32–35 (2005)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  16. Dunin-Kęplicz, B., Jankowski, A., Skowron, A., Szczuka, M.: Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS2004). Series in Soft Computing. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  17. Edghill, E.M.: On Interpretation by Aristotle (Trans.). eBooks@Adelaide (2007)

    Google Scholar 

  18. Feigenbaum, E., Feldman, J. (eds.): Computers and Thought. McGraw Hill, New York (1963)

    MATH  Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  21. Ganter, B., Stumme, G., Wille, R. (eds.): Formal Concept Analysis. LNCS (LNAI), vol. 3626. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  22. Garling, D.J.H.: A Course in Galois Theory. Cambridge University Press, New York (1987)

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  24. Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Morgan Kaufmann, San Francisco (2004)

    MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  26. 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)

    Chapter  Google Scholar 

  27. Jankowski, A., Skowron, A.: Logic for artificial intelligence: The Rasiowa-Pawlak school perspective. In: Ehrenfeucht, A., Marek, V., Srebrny, M. (eds.) Andrzej Mostowski and Foundational Studies, pp. 106–143. IOS Press, Amsterdam (2007)

    Google Scholar 

  28. Jankowski, A., Skowron, A.: Wisdom Granular Computing. In: [62], pp. 329–345 (2007)

    Google Scholar 

  29. 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 

  30. Johnstone, P.: Stone Spaces. Cambridge University Press, Cambridge (1986)

    MATH  Google Scholar 

  31. Kan, D.M.: Adjoint functors. Trans. Am. Math. Soc., Soc. 87, 294–329 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  32. Keefe, R.: Theories of Vagueness. Cambridge Studies in Philosophy. Cambridge (2000)

    Google Scholar 

  33. Kloesgen, W., Żytkow, J.: Handbook of Knowledge Discovery and Data Mining. Oxford University Press, New York (2002)

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  35. Lambek, J., Scott, P.J.: Introduction to Higher-Order Categorical Logic. Cambridge Studies in Advanced Mathematics, vol. 7. Cambridge University Press, Cambridge (1986)

    MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  39. Leśniewski, S.: On the foundations of mathematics. Topoi 2, 7–52 (1982)

    Google Scholar 

  40. Lin, T.Y.: Neighborhood systems and approximation in database and knowledge base systems. In: Emrich, M.L., Phifer, M.S., Hadzikadic, M., Ras, Z.W. (eds.) Proceedings of the Fourth International Symposium on Methodologies of Intelligent Systems (Poster Session), October 12–15, 1989, pp. 75–86. Oak Ridge National Laboratory, Charlotte (1989)

    Google Scholar 

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

    Book  Google Scholar 

  42. Liu, J., Daneshmend, L.K.: Spatial Reasoning and Planning: Geometry, Mechanism, and Motion. Springer, Berlin (2003)

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  44. MacLane, S.: Categories for the Working Mathematicians. Graduate Texts in Mathematics. Springer, Berlin (1997)

    Google Scholar 

  45. MacLane, S., Moerdijk, I.: Sheaves in Geometry and Logic: A First Introduction to Topos Theory. Universitext. Springer, Berlin (1994)

    Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  48. Mooney, R.: Learning to connect language and perception. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI), Senior Member Paper, Chicago, IL, July 13–17, 2008, pp. 1598–1601 (2008)

    Google Scholar 

  49. 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. 334–506. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  50. Nguyen, S.H., Bazan, J.G., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  51. Nguyen, H.S., Skowron, A., Stepaniuk, J.: Discovery of changes along trajectories generated by process models induced from data and domain knowledge. In: Lindemann, G., Burkhard, H.-D., Czaja, L., Penczek, W., Salwicki, A., Schlingloff, H., Skowron, A., Suraj, Z. (eds.) Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P 2008), September 29–October 1, 2008. Informatik-Berichte, Humboldt Universitaet zu Berlin, vol. 3, pp. 350–362. Gross Vaeter, Germany (2008)

    Google Scholar 

  52. Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: Rough set approach to sunspot classification problem. In: [91], pp. 263–272 (2005)

    Chapter  Google Scholar 

  53. Nguyen, T.T.: Eliciting domain knowledge in handwritten digit recognition. In: [57], pp. 762–767 (2005)

    Chapter  Google Scholar 

  54. Nguyen, T.T., Skowron, A.: Rough-granular computing in human-centric information processing. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modelling, Springer, Heidelberg (to appear 2009)

    Google Scholar 

  55. Ogden, C.K., Richards, I.A.: The Meaning of Meaning. A Study of the Influence of Language upon Thought and of the Science of Symbolism. Kegan Paul, Trench, Trubner and Co., Ltd, London (1923) see also this book: With an Introduction by Postgate, J. P., and Supplementary Essays by Malinowski, B., Crookshank, F.G., Harcourt Brace Jovanovich, Inc., New York (1989)

    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. Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.): PReMI 2005. LNCS, vol. 3776. Springer, Heidelberg (2005)

    Google Scholar 

  58. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  60. Pawlak, Z.: Concurrent versus sequential the rough sets perspective. Bulletin of the EATCS 48, 178–190 (1992)

    MATH  Google Scholar 

  61. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007); Rough sets: Some extensions. Information Sciences 177(1): 28–40; Rough sets and boolean reasoning. Information Sciences 177(1): 41–73 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  62. Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, New York (2008)

    Google Scholar 

  63. Peters, J.F.: Approximation spaces for hierarchical intelligent behavioural system models. In: Kęplicz, B.D., 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 

  64. Peters, J.F.: Rough ethology: Towards a biologically-inspired study of collective behavior 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)

    Chapter  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  66. Polkowski, L.: Rough Sets: Mathematical Foundations. Advances in Soft Computing. Physica-Verlag, Heidelberg (2002)

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  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), pp. 1–14. Publishing House of Electronic Industry, Beijing (2000)

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  71. Skowron, A.: Perception logic in intelligent systems (plenary talk). 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 (2005)

    Google Scholar 

  72. Skowron, A.: Rough sets in perception-based computing. 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.: Tolerance approximation spaces. Fundamenta Informaticae 27(2–3), 245–253 (1996)

    MathSciNet  MATH  Google Scholar 

  74. Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: [56], pp. 43–84 (2004)

    Chapter  Google Scholar 

  75. Skowron, A., Stepaniuk, J.: Rough sets and granular computing: Toward rough-ranular computing. In: [62], pp. 425–448 (2008)

    Chapter  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  77. Skowron, A., Świniarski, R.W.: Rough sets and higher order vagueness. In: [90], pp. 33–42 (2005)

    Chapter  Google Scholar 

  78. Skowron, A., Świniarski, R.W., Synak, P.: Approximation spaces and information granulation. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 175–189. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  79. Skowron, A., Szczuka, M.: Toward Interactive Computations: A Rough-Granular Approach. In: Koronacki, J., Wierzchon, S.T., Ras, Z.W., Kacprzyk, J. (eds.) Commemorative Volume to Honor Ryszard Michalski, Springer, Heidelberg (in preparation 2009)

    Google Scholar 

  80. Skowron, A., Synak, P.: Complex patterns. Fundamenta Informaticae 60(1–4), 351–366 (2004)

    MathSciNet  MATH  Google Scholar 

  81. Skowron, A., Synak, P.: Reasoning in information maps. Fundamenta Informaticae 59(2–3), 241–259 (2004)

    MathSciNet  MATH  Google Scholar 

  82. Skowron, A., Peters, J.F.: Rough-granular computing. In: [62], pp. 285–327 (2008)

    Chapter  Google Scholar 

  83. Smith, J.A, (Trans.): On the soul by Aristotle. eBooks@Adelaide (2007), http://etext.library.adelaide.edu.au/a/aristotle/

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

    Google Scholar 

  85. Sun, R.: Duality of the Mind: A Bottom-up Approach Toward Cognition. Lawrence Erlbaum, Mahwah (2001)

    Google Scholar 

  86. Sun, R. (ed.): Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation. Cambridge University Press, New York (2006)

    Google Scholar 

  87. Suraj, Z.: Rough set methods for the synthesis and analysis of concurrent processes. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications. Studies in Fuzziness and Soft Computing, vol. 56, pp. 379–488. Physica-Verlag, Heidelberg (2000)

    Chapter  Google Scholar 

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

    Google Scholar 

  89. Sycara, K.: Multiagent systems. AI Magazine 19(2), 79–93 (1998)

    Google Scholar 

  90. Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.): RSFDGrC 2005. LNCS (LNAI), vol. 3641. Springer, Heidelberg (2005)

    Google Scholar 

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

    Google Scholar 

  92. Tarski, A.: The Collected Papers of Alfred Tarski, 4 vols. In: Givant, S.R., McKenzie, R.N. (eds.), Birkhäuser, Basel (1986)

    Google Scholar 

  93. Turing, A.: Computing machinery and intelligence. Mind LIX(236), 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  94. Unnikrishnan, K.P., Ramakrishnan, N., Sastry, P.S., Uthurusamy, R.: 4th KDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data. The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data (KDD 2006), Philadelphia, USA, August 20–23, 2006 (2006), http://people.cs.vt.edu/~ramakris/kddtdm06/cfp.html

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

    MATH  Google Scholar 

  96. Ulam, S.M.: Analogies Between Analogies: The Mathematical Reports of S. M. Ulam and His Los Alamos Collaborators. University of California Press, Berkeley (1990)

    MATH  Google Scholar 

  97. Wittgenstein, L.: Philosophical Investigations. The German text, with revised English translation (Translated by Anscombe, G.E.M.). Blackwell, Oxford (2001)

    MATH  Google Scholar 

  98. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 333–353 (1965)

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  101. Zadeh, L.A.: Outline of a computational approach to meaning and knowledge representation based on the concept of a generalized assignment statement. In: Thoma, M., Wyner, A. (eds.) Proceedings of the International Seminar on Artificial Intelligence and Man-Machine Systems, pp. 198–211. Springer, Heidelberg (1986)

    Chapter  Google Scholar 

  102. 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)

    Article  MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  104. Zadeh, L.A.: Foreword. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies Series, pp. IX–XI. Springer, Berlin (2004)

    Google Scholar 

  105. Zadeh, L.A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics and Data Analysis 51, 15–46 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  106. 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 

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

    MATH  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Decision Processes Support and AdgaM Solutions Sp. z o.o., Wąwozowa 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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Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul J.K. Ordona 21, 01-237, Warsaw, Poland

    Małgorzata Marciniak

  2. Institute of Computer Science, Polish Academy of Sciences, J. K. Ordona 21, 01-237, Warsaw, Poland

    Agnieszka Mykowiecka

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Jankowski, A., Skowron, A. (2009). Wisdom Technology: A Rough-Granular Approach. In: Marciniak, M., Mykowiecka, A. (eds) Aspects of Natural Language Processing. Lecture Notes in Computer Science, vol 5070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04735-0_1

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