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Generalized Quantifiers in the Context of Rough Set Semantics

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EN
Abstrakty
EN
Looking back to Prof. Zadeh’s paradigm of Computing withWords (CWW) [28, 29, 30], one can notice that the initial attempt of such an endeavour was to set up a basic vocabulary of linguistic words, and fix their semantics based on fuzzy sets. Then a grammar was proposed to generate compound linguistic expressions based on the primitive ones, and simultaneously based on the semantic interpretations of those basic linguistic expressions a general scheme for the semantics of the rest of linguistic expressions were proposed. Sentences involving linguistic quantifiers and vague predicates constitute a fragment of natural language. In this paper, we choose this fragment of the natural language, and explore the semantics from the perspective of rough sets [13, 14, 16, 17, 18, 21]. We fix a set of basic crisp quantifiers, mainly of proportional kind. A set of vague quantifiers are proposed to lie in a close vicinity of those crisp quantifiers in the sense that a particular vague quantifier can be visualized as a blurred, may be called rough, image of a set of crisp quantifiers. Semantics of the rest of the vague quantifiers can be obtained based on the subjective perception of the interrelations among the (vague) quantifiers.
Wydawca
Rocznik
Strony
213--236
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
  • Institute of Mathematics, University of Warsaw Warsaw, Poland and Vistula University, Warsaw, Poland
autor
  • Institute of Mathematics, University of Warsaw Warsaw, Poland and Systems Research Institute, Polish Academy of Sciences Newelska 6, 01–447 Warsaw, Poland
Bibliografia
  • [1] Banerjee, M., Chakraborty, M.K.: Rough logics: a survey with further directions, In Studies in Fuzziness and Soft Computing, Incomplete Information: Rough Set Analysis (Ewa Orłowska, Ed.), 1991, 570–600.
  • [2] Banerjee, M., Chakraborty, M.K.: Rough sets through algebraic logic, Fundamenta Informaticae, 28(3–4), 1996, 211–221.
  • [3] Benthem, J. van: Questions about quantifiers, Journal of Symbolic Logic, 49(2), 1984, 443–466.
  • [4] Barwise, J., Cooper, R.: Generalized quantifiers and natural language, Linguistics and Philosophy, 4, 1981, 159–219.
  • [5] Chakraborty, M.K., Banerjee, M.: Rough logic with rough quantifiers, Bull. Polish Acad. Sc. (Math.), 41(4), 1993, 305–315.
  • [6] Chakraborty, M.K., Banerjee, M.: Rough dialogue and implication lattices, Fundamenta Informaticae, 75 (1-4), 2007, 123–139.
  • [7] Chakraborty, M.K., Banerjee, M.: Rough Sets: Some Foundational Issues, Fundamenta Informaticae, 127, 2013, 1–15.
  • [8] Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg, 2001.
  • [9] Glöckner, Igno: Fuzzy quantifiers: a computational theory, Studies in Fuzziness and Soft Computing, Vol. 193, Springer-Verlag Berlin Heidelberg, 2006.
  • [10] Krynicki, M., Tuschik, H-P.: An axiomatization of the logic with the rough quantifier, J. Symb. Logic, 56(2), 1991, 608–617.
  • [11] Nguyen, S. Hoa, Bazan, J., Skowron, A., Nguyen, H. Son: Layered learning for concept synthesis, Transactions on Rough Sets I: Journal Subline LNCS 3100, 2004, 187–208.
  • [12] Pagliani, P.: Rough set theory and logic-algebraic structures, In Incomplete Information: Rough Set Analysis, (Ewa Orłowska, Ed.), Volume 13 of Studies in Fuzziness and Soft Computing, Springer Physica-Verlag, 1998, 109–190.
  • [13] Pawlak, Z.: Rough sets, Int. J. Comp. Inf. Sci. 11, 1982, 341–356.
  • [14] Pawlak, Z.: Rough sets, Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, 1991.
  • [15] Pawlak, Z., Skowron, A.: Rough membership functions, In Advances in the Dempster-Shafer Theory of Evidence, Ronald Yager, Fedrizzi, M. and Janusz Kacprzyk (eds.), John Wiley & Sons, New York NY, 1994, 251–271.
  • [16] Pawlak, Z., Skowron, A.: Rudiments of rough sets, Information Sciences, 177(1), 2007, 3–27.
  • [17] Pawlak, Z., Skowron, A.: Rough sets: Some extensions, Information Sciences, 177(1), 2007, 28–40.
  • [18] Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning, Information Sciences, 177(1), 2007, 41–73.
  • [19] Peters, J.F., Skowron, A., Stepaniuk, J.: Rough sets: foundations and perspectives, In Encyclopedia of Complexity and System Science, R.A. Meyers (ed.), Springer-Verlag, 2009, 7787–7797.
  • [20] Ralescu, D.: Cardinality, quantifiers, and the aggregation of fuzzy criteria, Fuzzy Sets and Systems 69, 1995, 355–365.
  • [21] Skowron, A., Suraj Z.: Rough Sets and Intelligent Systems. Professor Zdzislaw Pawlak in Memoriam. Series Intelligent Systems Reference Library, Vol. 42–43, Springer, Heidelberg, 2013.
  • [22] Tamir, D.E., Rishe, N.D., Kandel, A.: Fifty Years of Fuzzy Logic and its Applications. Studies in Fuzziness and Soft Computing 326, Springer, Heidelberg, 2015.
  • [23] Yager, R.R.: Reasoning with fuzzy quantified statements. Part-I, Kybernetes 14, 1985, 233–240.
  • [24] Yager, R.R.: Reasoning with fuzzy quantified statements. Part-II, Kybernetes 15, 1985, 111–120.
  • [25] Yao, Y. Y.: Constructive and algebraic methods of the theory of rough sets in finite universes. Int. J. Approx. Reasoning, 15(4), 1996, 291–317.
  • [26] Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages, Computers and Mathematics with Applications, 9, 1983, 149–184.
  • [27] Zadeh, L.A.: A computational theory of dispositions, International Journal of Intelligent Systems, 2(1), 1987, 39–63.
  • [28] 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), 1999, 105–119.
  • [29] Zadeh, L.A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics and Data Analysis, 51, 2006, 15–46.
  • [30] Zadeh, L.A.: Computing with Words: Principal Concepts and Ideas. Studies in Fuzziness and Soft Computing, Vol. 277, Springer, Heidelberg, 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d93c3de3-fcd5-40a6-a5df-393cd7c0831c
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