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On-Line Elimination of Non-relevant Parts of Complex Objects in Behavioral Pattern Identification

  • Jan G. Bazan
  • Andrzej Skowron
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

We discuss some rough set tools for perception modelling that have been developed in our project for a system for modelling networks of classifiers for compound concepts. Such networks make it possible to recognize behavioral patterns of objects and their parts changing over time. We present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. Some results of experiments with data from the road simulator are included.

Keywords

Temporal Pattern Behavioral Pattern Multiagent System Temporal Window Complex Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Anderson, J.R.: Rules of the mind. Lawrence Erlbaum, Hillsdale (1993)Google Scholar
  2. 2.
    Bar-Yam, Y.: Dynamics of Complex Systems. Addison Wesley, Reading (1997)zbMATHGoogle Scholar
  3. 3.
    Bazan, J., Skowron, A.: Classifiers based on approximate reasoning schemes. In: Dunin-Keplicz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security, and Rescue Tasks in Multiagent Systems MSRAS. Advances in Soft Computing, pp. 191–202. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 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) (to appear)CrossRefGoogle Scholar
  5. 5.
    Bazan, J., Nguyen, S.H., Nguyen, H.S., Skowron, A.: Rough set methods in approximation of hierarchical concepts. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 346–355. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Kieras, D., Meyer, D.E.: An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction 12, 391–438 (1997)CrossRefGoogle Scholar
  7. 7.
    Langley, P., Laird, J.E.: Cognitive architectures: Research issues and challenges. Technical Report, Institute for the Study of Learning and Expertise, Palo Alto, CA (2002)Google Scholar
  8. 8.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence. Artificial Intelligence 33, 1–64 (1987)CrossRefGoogle Scholar
  9. 9.
    Luck, M., McBurney, P., Preist, C.: Agent Technology: Enabling Next Generation. A Roadmap for Agent Based Computing. Agent Link (2003)Google Scholar
  10. 10.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  11. 11.
    Nguyen, S.H., Bazan, J., 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)CrossRefGoogle Scholar
  12. 12.
    Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  14. 14.
    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(LNAI), vol. 3400, pp. 153–174. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    The Road simulator Homepage - http://logic.mimuw.edu.pl/~bazan/simulator
  16. 16.
    The RSES Homepage – http://logic.mimuw.edu.pl/~rses
  17. 17.
    Veloso, M.M., Carbonell, J.G.: Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization. Machine Learning 10, 249–278 (1993)CrossRefGoogle Scholar
  18. 18.
    Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jan G. Bazan
    • 1
  • Andrzej Skowron
    • 2
  1. 1.Institute of MathematicsUniversity of RzeszówRzeszówPoland
  2. 2.Institute of MathematicsWarsaw UniversityWarsawPoland

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