Rough Set Approach to Behavioral Pattern Identification

• Autorzy: Bazan J.G. , Bazan-Socha S. , Pietrzyk J.J. , Kruczek P. , Skowron A.
• Języki publikacji: EN

Abstrakty

EN

The problem considered is how to model perception and identify behavioral patterns of objects changing over time in complex dynamical systems. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzisaw Pawlak during the early 1980s provides the foundation for the construction of classifiers, relative to what are known as temporal pattern tables. Temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to identify behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning about concepts embedded in such an ontology. We also 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. The article includes results of experiments that have been performed on data from a vehicular traffic simulator and on medical data obtained from Neonatal Intensive Care Unit in the Department of Pediatrics, Collegium Medicum, Jagiellonian University. The contribution of this article is the introduction of a network of classifiers that make it possible to identify the behavioral patterns of objects that change over time.

Słowa kluczowe

EN

behavioral pattern; rough sets; concept approximations; complex dynamical system; respiratory failure

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2007
• Tom: Vol. 75, nr 1-4
• Strony: 27--47
• Opis fizyczny: bibliogr. 32 poz., wykr.

Twórcy

autor

Bazan J.G.

autor

Bazan-Socha S.

autor

Pietrzyk J.J.

autor

Kruczek P.

autor

Skowron A.

• Institute of Mathematics, University of Rzeszów, Rejtana 16A, 35-959 Rzeszów, Poland,

Bibliografia

• [1] Altman, D., G.: Practical Statistics for Medical Research. Chapman and Hall/CRC, London, 1997.
• [2] Anderson, J., R.: Rules of the mind. Lawrence Erlbaum, Hillsdale, NJ, 1993.
• [3] Bar-Yam, Y.: Dynamics of Complex Systems. Addison Wesley, Reading, MA, 1997.
• [4] Bazan, J., Skowron, A.: Classifiers based on approximate reasoning schemes. In Dunin-Keplicz, B., Jankowski, A., Skowron, A., and Szczuka, M. (Eds.): Monitoring, Security, and Rescue Tasks in Multiagent Systems MSRAS, Advances in Soft Computing. Springer, Heidelberg, 2005,191-202.
• [5] Bazan, J., Nguyen, Hoa S., Nguyen, Son H., Skowron, A.: Rough set methods in approximation of hierarchical concepts. In Proceedings of RSCTC'2004, LNAI 3066, Springer, Heidelberg, 2004, 346-355.
• [6] Bazan, J., Peters, J. F., Skowron, A.: Behavioral pattern identification through rough set modelling. In Proceedings of RSFDGrC'2005, LNAI 3641, Springer, Heidelberg, 2005, 688-697.
• [7] Bazan, J., G., 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, 2650-2657.
• [8] Birattari, M., Di Caro, G., Dorigo, M.: Toward the formal foundation of ant programming. LNCS 2463. Springer-Verlag, Berlin, 2002, 188-201.
• [9] Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems, Oxford University Press, UK, 1999.
• [10] Dorigo, M., Birattari, M., Blum, C., Gambardella, L. M., Monada, F., Stutzle, T. (Eds.): Ant Colony Optimization and Swarm Intelligence. LNCS 3172. Springer-Verlag, Berlin, 2004.
• [11] Fahle, M, Poggio, T. (Eds.): Perceptual Learning, The MIT Press, Cambridge, MA, 2002.
• [12] Harnad, S. (Ed.): Categorical Perception. The Groundwork of cognition, Cambridge University Press, UK, 1987.
• [13] 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, 1997, 391-438.
• [14] Laird, J. E., Newell, A., Rosenbloom, P. S.: Soar: An architecture for general intelligence. Artificial Intelligence 33, 1987, 1-64.
• [15] 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.
• [16] Luck, M., McBurney, P., Preist Ch.: Agent technology: Enabling Next generation. A roadmap for Agent Based Computing. Agent Link, 2003.
• [17] Newell, A.: Unified Theories of Cognition, Cambridge, Harvard University Press, MA, 1990.
• [18] Nguyen, Hoa S., Bazan, J., Skowron, A., Nguyen, Son H.: Layered learning for concept synthesis. In LNCS 3100, Transactions on Rough Sets I, Springer, Heidelberg, 2004, 187-208.
• [19] Pal, S. K., Polkowski, L., Skowron, A. (Eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg, 2004.
• [20] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving 9. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991.
• [21] Peters, J. F.: Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces. In Transactions on Rough Sets III, LNCS 3400, 2005, 153-174.
• [22] Peters, J. F.: Approximation spaces for hierarchical intelligent behavioral system models. In Dunin-Keplicz, B., Jankowski, A., Skowron, A., and Szczuka, M. (Eds.), Monitoring, Security, and Rescue Tasks in Multiagent Systems MSRAS, Advances in Soft Computing, Springer, Heidelberg, 2004, 13-30.
• [23] Peters, J. F., Henry, C., Ramanna, S.: Rough Ethograms : Study of Intelligent System Behavior. In Proceedings of IIS05, Gdańsk, Poland, June, 2005, 13-16.
• [24] Peters, J.F., Henry, C.: Reinforcement learning with approximation spaces. Fundamenta Informaticae 71 (2-3), 2006, 323-349.
• [25] Peters, J.F.: Approximation spaces in off-policy Monte Carlo learning. Plenary paper in T. Burczynski, W. Cholewa, W. Moczulski (Eds.), Recent Methods in Artificial Intelligence Methods, AI-METH Series, Gliwice, 2005, 139-144.
• [26] Sutton, R. S., Barto, A. G.: Reinforcement Learning: An Introduction. Cambridge, MA: The MIT Press, 1998.
• [27] Urmson, C. et al.: High speed navigation of unrehearsed terrain: Red team technology for Grand Challenge. Report CMU-RI-TR-04-37, The Robotics Institute, Carnegie Mellon University, 2004.
• [28] Veloso, M., M., Carbonell, J., G.: Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization. Machine Learning 10, 1993, 249-278.
• [29] Vincent, JL., de Mendonca, A., Cantraine, F., et al.: Use of the SOFA score to assess the incidence of organ dysfunction. In Crit Care Medicine 26, 1998, 1793-1800.
• [30] Zadeh, L., A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 2004, 73-84.
• [31] The Road simulator Homepage at logic.mimuw.edu.pl/_bazan/simulator
• [32] The RSES Homepage at logic.mimuw.edu.pl/_rses