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From Rough Sets to Rough Knowledge Bases

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents an expressive language for representing knowledge about vague concepts. It is based on the rough set formalism and it caters for implicit definition of rough relations by combining different regions (e.g. upper approximation, lower approximation, boundary) of other rough relations. The semantics of the proposed language is obtained by translating it to the language of extended logic programs whose meaning is captured by paraconsistent stable models. A query language is also discussed to retrieve information about the defined rough relations. Motivating examples illustrating the use of the language are described.
Wydawca
Rocznik
Strony
215--246
Opis fizyczny
Bibliogr. 21 poz., tab.
Twórcy
autor
  • Department of Science and Technology, Linköping University, S-601 74 Norrköping, Sweden
  • Centro de Inteligência Artificial (CENTRIA), Dept. Informática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
  • Dept. of Computer and Information Science, Linköping University, S 581 83 Linköping, Sweden
Bibliografia
  • [1] Alferes, J. J., Leite, J. A., Pereira, L. M., Przymusinska, H., Przymusiski, T. C.: Dynamic Updates of Nonmonotonic Knowledge Bases, Journal of Logic Programming, number 45, Elsevier, 2000.
  • [2] Almukdad, A., Nelson, D.: Constructible Falsity and Inexact Predicates, Journal of Symbolic Logic, 49, 1984, 231-233.
  • [3] Apt, K., Bol, R.: Logic Programming and Negation: A Survey, Journal of Logic Programming, 19/20, Elsevier, May/July 1994.
  • [4] Doherty, P., Lukaszewicz, W., Szalas, A.: CAKE: A Computer Aided Knowledge Engineering Technique, Proc. of the 15th European Conference on Artificial Intelligence, (ECAI’02), IOS Press, Amsterdam, 2002.
  • [5] Eiter, T., Leone, N., Mateis, C., Pfeifer, G., Scarcello, F.: The KR System dlv: Progress Report, Comparisons and Benchmarks, KR’98: Principles of Knowledge Representation and Reasoning (A. G. Cohn, L. Schubert, S. C. Shapiro, Eds.), Morgan Kaufmann, San Francisco, California, 1998.
  • [6] Gelfond, M., Lifschitz, V.: The Stable Model Semantics for Logic Programming, Proc. of the 5th International Conference on Logic Programming (R. A. Kowalski, K. Bowen, Eds.), MIT Press, Seattle, USA, 1988.
  • [7] Gelfond, M., Lifschitz, V.: Logic programs with Classical Negation, Proc. of the 7th International Conference on Logic Programming (Warren, Szeredi, Eds.), MIT Press, 1990.
  • [8] Inoue, K., Sakama, C.: Negation as Failure in the Head, Journal of Logic Programming, 35(1), 1998, 39-78.
  • [9] Komorowski, J., Øhrn, A.: Modelling Prognostic Power of Cardiac Tests using Rough Sets, Journal of Artificial Intelligence in Medicine, 15(2), 1999, 167-191.
  • [10] Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial, Rough Fuzzy Hybridization. A New Trend in Decision-Making, Springer-Verlag, 1999.
  • [11] Niemelä, I., Simons, P.: Efficient Implementation of the Well-founded and Stable Model Semantics, Proc. of the Joint International Conference and Symposium on Logic Programming (M. Maher, Ed.), MIT Press, Bonn, Germany, 1996.
  • [12] Pawlak, Z.: Rough Sets, International Journal of Information and Computer Science, 11(5), 1982, 341-356.
  • [13] Pawlak, Z.: Rough sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
  • [14] Pearce, D.: Answer sets and constructive logic, II: Extended logic programs and related non-monotonic formalisms, Logic Programming and Nonmonotonic Reasoning - Proc. of the 2nd International Workshop (L. Pereira, A. Nerode, Eds.), MIT Press, 1993.
  • [15] Reiter, R.: A Logic for Deafult Reasoning, Journal of Artificial Intelligence, 13, 1980, 81-132.
  • [16] Sakama, C., Inoue, K.: Paraconsistent Stable Semantics for Extended Disjunctive Programs, Journal of Logic and Computation, 5(3), 1995, 265-285.
  • [17] Vitória, A., Damásio, C. V., Małuszyński, J.: Query Answering for Rough Knowledge Bases, Proc. of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC’03, Chongqing, China (G. Wang, Q. Liu, Y. Yao, A. Skowron, Eds.), 2639, Springer, 2003, ISBN 3-540-14040-9.
  • [18] Vitória, A., Małuszyński, J.: A Logic Programming Farmework for Rough Sets, Proc. of the 3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC’02 (J. Alpigini, J. Peters, A. Skowron, N. Zhong, Eds.), number 2475 in LNCS/LNAI, Springer-Verlag, 2002.
  • [19] Ziarko, W.: Variable Precision Rough Set Model, Journal of Computer and Systems Science, 46(1), 1993, 39-59.
  • [20] Ziarko, W.: Acquisition of Hierarchy-Structured Probabilistic Decision Tables and Rules from Data, Proc. of the World Congress on Computational Intelligence, Honolulu, 2002.
  • [21] Ziarko, W., Fei, X.: VPRSM Approach to WEB Searching, Proc. of the 3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC’02 (J. Alpigini, J. Peters, A. Skowron, N. Zhong, Eds.), number 2475 in LNCS/LNAI, Springer-Verlag, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0004-0150
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