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Complex Patterns

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We outline some results of our current research on developing a methodology for solving problems of spatio-temporal reasoning. We consider classifiers for complex concepts in spatio-temporal reasoning that are constructed hierarchically. We emphasise the fact that the construction of such hierarchical classifiers should be supported by domain knowledge. Approximate reasoning networks (AR networks) are proposed for approximation of reasoning schemes expressed in natural language. Such reasoning schemes are extracted from knowledge bases representing domain knowledge. This approach makes it possible to induce classifiers for complex concepts by constructing them along schemes of reasoning extracted from domain knowledge.
Wydawca
Rocznik
Strony
351--366
Opis fizyczny
Bibliogr. 40 poz., wykr.
Twórcy
autor
  • Institute of Informatics, Warsaw University, ul. Banacha 2, 02-097 Warsaw, Poland
autor
  • Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland
Bibliografia
  • [1] Barsalou. L. W.: Perceptual Symbol Systems, Behavioral and Brain Sciences, 22, 1999, 577-660.
  • [2] Barwise, J., Seligman, J.. Eds.: Information Flow: The Logic of Distributed Systems, vol. 44 of Tracts in Theoretical Computer Science, Cambridge University Press, Cambridge. UK. 1997.
  • [3] Bennett. B., Cohn, A. G., Wolter, F., Zakharyaschev, M.: Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning, Applied Intelligence. 17(3), 2002, 239-251.
  • [4] Breiman. L.: Statistical Modeling: The Two Cultures, Statistical Science, 16(3), 2001, 199-231.
  • [5] Escrig, М. Т., Toledo, F.: Qualitative Spatial Reasoning: Theory and Practice, IOS Press, Amsterdam, 1998.
  • [6] Fahle. M., Poggio, Т.: Perceptual Learning, MIT Press, 2002.
  • [7] Friedman. J. H., Hastie. Т., Tibshirani, R.: The Elements of Statistical Learning: Data Mining. Inference, and Prediction, Springer-Verlag, Heidelberg. 2001.
  • [8] Hamad, S.: Categorical Perception: The Groundwork of Cognition, Cambridge University Press, New York, NY, 1987.
  • [9] Kloesgen, W., Żytkow, J., Eds.: Handbook of Knowledge Discovery and Data Mining, Oxford University Press, 2002.
  • [10] McCarthy, J.: Notes on Formalizing Context. Thirteenth International Joint Conference on Artificial Intelligence 1JCAI, Morgan Kaufmann. Chambéry, France, 1993.
  • [11] McCarthy, J.. Hayes, P.: Some Philosophical Problems from the Standpoint of Artificial Intelligence, Machine Intelligence, 4, 1969.463-502.
  • [12] Mitchell. Т. M.: Machine Learning, Mc Graw Hill, 1997.
  • [13] Pal, S. K., Polkowski, L., Skowron, A., Eds.: Rough-Neural Computing: Techniques for Computing with Words, Cognitive Technologies, Springer-Verlag, Heidelberg, 2004.
  • [14] Pal, S. K., Skowron. A., Eds.: Rough Fuzzy Hybridization: A New Trend in Decision-Making, Springer-Verlag, Singapore, 1999.
  • [15] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. vol. 9 of System Theory. Knowledge Engineering and Problem Solving, Kluwer Academic Publishers, Dordrecht. 1991.
  • [16] Peters, J. F., Skowron. A., Synak. P., Ramanna, S.: Rough Sets and Information Granulation, Tenth International Fuzzy Systems Association World Congress IFSA (T. Bilgic. D. Baets, O. Kaynak, Eds.), Lecture Notes in Artificial Intelligence. 2715, Springer-Verlag, Heidelberg, 2003, 370-377.
  • [17] Poggio, Т., Smale, S.: The Mathematics of Learning: Dealing with Data, Notices of the AMS, 50(5), 2003, 537-544.
  • [18] Polkowski, L., Skowron, A.: Rough Mereological Approach to Knowledge-Based Distributed AI. Third World Congress on Expert Systems (J. K. Lee, J. Liebowitz, J. M. Chae. Eds.), Cognizant Communication Corporation. Soeul, Korea, February 5-9, 1996. 774-781.
  • [19] Polkowski. L., Skowron. A.: Rough Mereology: A New Paradigm for Approximate Reasoning, International Journal of Approximate Reasoning, 15(4), 1996, 333-365.
  • [20] Polkowski, L., Skowron. A.: Towards Adaptive Calculus of Granules, Computing with Words in Information/Intelligent Systems (L. A. Zadeh, J. Kacprzyk, Eds.), Physica-Verlag. Heidelberg, 1999, 201-227.
  • [21] Polkowski, L., Skowron. A.: Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning, in: Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems (L. Polkowski. T. Y. Lin. S. Tsumoto, Eds.), Studies in Fuzziness and Soft Computing, 56. Springer-Verlag, Heidelberg, 2000. 89-135.
  • [22] Polkowski, L., Skowron, A.: Rough Mereological Calculi of Granules: A Rough Set Approach to Computation, Computational Intelligence, 17(3), 2001, 472^492.
  • [23] Roddick, J. F., Hornsby, K., Spiliopoulou. M.: An Updated Bibliography of Temporal, Spatial and Spatio-Temporal Data Mining Research, Post-Workshop Proceedings of the International Workshop on Temporal Spatial and Spatio-Temporal Data Mining TSDM (J. F. Roddick, K. Hornsby, Eds.), Lecture Notes in Artificial Intelligence, 2007. Springer-Verlag, Berlin, 2001, 147-163.
  • [24] Roddick, J. F., Hornsby, K., Spiliopoulou, M.: YABTSSTDMR - Yet Another Bibliography of Temporal, Spatial and Spatio-Temporal Data Mining Research, SIGKDD Temporal Data Mining Workshop (K. P. Unnikrishnan. R. Uthurusamy, Eds.), ACM Press, San Francisco, CA, 2001, 167-175.
  • [25] Sandewall, E., Ed.: Features and Fluents: The Representation of Knowledge About Dynamical Systems, vol. I. Oxford University Press, 1994.
  • [26] Skowron. A.: Toward Intelligent Systems: Calculi of Information Granules, Bulletin of the International Rough Set Society. 5(1-2). 2001.9-30.
  • [27] Skowron. A., Stepaniuk, J.: Tolerance Approximation Spaces. Fundamenta Informaticae, 27(2-3), 1996. 245-253.
  • [28] Skowron. A., Stepaniuk, J.: Information Granule Decomposition, Fundamenta Informaticae. 47(3-4), 2001, 337-350.
  • [29] Skowron. A., Stepaniuk. J.: Information Granules: Towards Foundations of Granular Computing, International Journal of Intelligent Systems. 16(1). 2001, 57-86.
  • [30] Skowron. A., Stepaniuk, J.: Information Granules and Rough-Neural Computing, in: Pal et al. [13], 43-84.
  • [31] Skowron. A., Stepaniuk. J., Peters, J.: Rough Sets and Infomorphisms: Towards Approximation of Relations in Distributed Environment, Fundamenta Informaticae. 54(2-3), 2003, 263-277.
  • [32] Skowron, A., Synak, P.: Patterns in Information Maps. Third International Conference on Rough Sets and Current Trends in Computing RSCTC (J. J. Alpigini. J. F. Peters. A. Skowron, N. Zhong, Eds.), Lecture Notes in Artificial Intelligence, 2475, Springer-Verlag, Heidelberg, 2002, 453-460.
  • [33] Skowron, A., Synak, P: Reasoning in Information Maps, Fundamenta Informaticae. 59(2-3), 2004, 241-259.
  • [34] SPACENET: Project Web Site, WWW. URL http://agora.scs.leeds.ac.uk/spacenet/
  • [35] Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge. MA, 2000.
  • [36] Świniarski, R., Skowron. A.: Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters, 24(6), 2003, 833-849.
  • [37] Vapnik, V.: Statistical Learning Theory'. John Wiley & Sons, New York. NY, 1998.
  • [38] WITAS: Project Web Site. WWW. URL http://www.ida.liu.se/ext/witas/eng.html
  • [39] Zadeh, L. A.: Fuzzy Sets, Information and Control, 8(3), 1965, 338-353.
  • [40] Zadeh, L. A.: A New Direction in AI: Toward a Computational Theory of Perceptions, AI Magazine. 22(1), 2001,73-84.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0044
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