Abstract
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 Zdzisław 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 modelling 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 on concepts embedded in such an ontology. This article also includes examples based on data from a vehicular traffic simulator useful in the identification of behavioral patterns by drivers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bar-Yam, Y.: Dynamiocs of Complex Systems. Addison Wesley, Reading (1997)
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)
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)
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)
Birattari, M., Di Caro, G., Dorigo, M.: Toward the formal foundation of ant programming. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 188–201. Springer, Heidelberg (2002)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, UK (1999)
Fahle, M., Poggio, T. (eds.): Perceptual Learning. The MIT Press, Cambridge (2002)
Harnad, S. (ed.): Categorical Perception. The Groundwork of cognition. Cambridge University Press, UK (1987)
Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2004)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. In: Volume 9 of System Theory, Knowledge Engineering and Problem Solving, Kluwer Academic Publishers, Dordrecht (1991)
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, vol. 3400, pp. 153–174. Springer, Heidelberg (2005)
Peters, J.F.: Approximation spaces for hierarchical intelligent behavioral system models. In: Dunin-Keplicz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 13–30. Physica, Heidelberg (2004)
Peters, J.F., Henry, C., Ramanna, S.: Rough Ethograms: Study of Intelligent System Behavior. In: New Trends in Intelligent Information Processing and Web Mining (IIS 2005), Gdańsk, Poland, June 13-16 (2005)
Peters, J.F., Henry, C., Ramanna, S.: Reinforcement learning with pattern-based rewards. In: Computational Intelligence (CI), Calgary, Alberta, Canada (July 2005) (to appear)
Luck, M., McBurney, P.: Preist, Ch.: Agent Technology: Enabling Next Generation. A Roadmap for Agent Based Computing. Agent Link (2003)
Road simulator Homepage at http://logic.mimuw.edu.pl/~bazan/simulator
RSES Homepage at http://logic.mimuw.edu.pl/~rses
Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.): ANTS 2004. LNCS, vol. 3172. Springer, Heidelberg (2004)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (1998)
Urmson, C., et al.: High speed navigation of unrehearsed terrain: Red team technology for Grand Challenge 2004. Report CMU-RI-TR-04-37, The Robotics Institute, Carnegie Mellon University (2004)
Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bazan, J.G., Peters, J.F., Skowron, A. (2005). Behavioral Pattern Identification Through Rough Set Modelling. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_73
Download citation
DOI: https://doi.org/10.1007/11548706_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
eBook Packages: Computer ScienceComputer Science (R0)