Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This paper introduces an approach to behavioral pattern identification as a part of a study of temporal patterns in complex dynamical systems. 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. It is quite remarkable that temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces what are known as behavior graphs. Temporal concepts approximated by approximate reasoning schemes become nodes in behavioral graphs. In addition, we discuss some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to recognize 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 useful in the identification of behavioral patterns by drivers.
Wydawca
Czasopismo
Rocznik
Tom
Strony
37--50
Opis fizyczny
wykr., bibliogr. 29 poz.
Twórcy
autor
- Institute of Mathematics, University of Rzeszów, Rejtana 16A, 35-959 Rzeszów, Poland, bazan@univ.rzeszow.pl
Bibliografia
- [1] Anderson, J. R.: Rules of the mind. Lawrence Erlbaum, Hillsdale, NJ, 1993.
- [2] Bar-Yam, Y.: Dynamics of Complex Systems. AddisonWesley, Reading, MA, 1997.
- [3] 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.
- [4] 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.
- [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] Birattari, M., Di Caro, G., Dorigo, M.: Toward the formal foundation of ant programming. LNCS 2463. Springer-Verlag, Berlin, 2002, 188-201.
- [7] Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems, Oxford University Press, UK, 1999.
- [8] 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.
- [9] Fahle, M, Poggio, T. (Eds.): Perceptual Learning, The MIT Press, Cambridge,MA, 2002.
- [10] Harnad, S. (Ed.): Categorical Perception. The Groundwork of cognition, Cambridge University Press, UK, 1987.
- [11] 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.
- [12] Laird, J. E., Newell, A., Rosenbloom, P. S.: Soar: An architecture for general intelligence. Artificial Intelligence 33, 1987, 1-64.
- [13] 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.
- [14] Luck, M., McBurney, P., Preist Ch.: Agent technology: Enabling Next generation. A roadmap for Agent Based Computing. Agent Link, 2003.
- [15] Newell, A.: Unified Theories of Cognition, Cambridge, Harvard University Press, MA, 1990.
- [16] 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.
- [17] Pal, S. K., Polkowski, L., Skowron, A. (Eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg, 2004.
- [18] 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.
- [19] 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.
- [20] 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.
- [21] Peters, J. F., Henry, C., Ramanna, S.: Rough Ethograms : Study of Intelligent System Behavior. In Proceedings of IIS05, Gdańsk, Poland, June, 2005, 13-16.
- [22] Peters, J.F., Henry, C.: Reinforcement learning with approximation spaces, Fundamenta Informaticae 2006 [to appear].
- [23] 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.
- [24] Sutton, R. S., Barto, A. G.: Reinforcement Learning: An Introduction. Cambridge, MA: The MIT Press, 1998.
- [25] 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.
- [26] Veloso, M., M., Carbonell, J., G.: Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization. Machine Learning 10, 1993, 249-278.
- [27] Zadeh, L., A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 2004, 73-84.
- [28] The Road simulator Homepage at logic.mimuw.edu.pl/~bazan/simulator.
- [29] The RSES Homepage at logic.mimuw.edu.pl/rses.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0010-0052