Abstract
Rule-based evolutionary systems like learning classifier system are widely used in industry and automation. In some types of problems we have additional information about problem solution. This information can be used in the process of problem solutions. A rule-based system can be augmented by additional information concerning the given problem to enrich the process of system adaptation to solve the problem. In many pattern matching tasks we know the patterns and we are looking for a pattern identification in environment. We provide representative of a rule-based learning classifier system augmented with information about a property of solution. The augmented system solves an example of pattern matching problem of simple Battleship game. This modified learning classifier system provides better convergence results by using the probability model of the Battleship game problem space.
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References
Abraham, A.: Hybrid soft computing and applications. International Journal of Computational Intelligence and Applications 8(1), 5–7 (2009)
Bull, L.: Learning classifier systems: A brief introduction. In: Applications of Learning Classifier Systems, p. 14. Springer (2004)
Bull, L., Kovacs, T.: Foundations of learning classifier systems. STUDFUZZ. Springer (2005)
Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design. STUDFUZZ, vol. 109. Springer (2006)
Corchado, A., Arroyo, A., Tricio, V.: Soft computing models to identify typical meteorological days. Logic Journal of the IGPL 19(2), 373–383 (2011)
Drugowitsch, J.: Design and Analysis of Learning Classifier Systems: A Probabilistic Approach. SCI. Springer (2008)
Halavati, R., Shouraki, S.B., Lotfi, S., Esfandiar, P.: Symbiotic evolution of rule based classifier systems. International Journal on Artificial Intelligence Tools 18(1), 1–16 (2009)
Harmon, M., Harmon, S.: Reinforcement learning: A tutorial (1996), http://www.nbu.bg/cogs/events/2000/Readings/Petrov/rltutorial.pdf
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)
Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 2003-2005. LNCS (LNAI), vol. 4399. Springer, Heidelberg (2007)
Kriesel, D.: A Brief Introduction to Neural Networks, Zeta version (2007), http://www.dkriesel.com
Lanzi, P.L.: Learning classifier systems: then and now. Evolutionary Intelligence 1(1), 63–82 (2008)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): Learning Classifier Systems: From Foundations to Applications. LNCS (LNAI), vol. 1813. Springer, Heidelberg (2000)
Ruta, D., Gabrys, B.: An Overview of Classifier Fusion Methods (2000)
Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2010)
Sigaud, O., Wilson, S.W.: Learning classifier systems: A survey. Soft Computing 11(11), 1065–1078 (2007)
Smith, M.: Neural Networks for Statistical Modeling. Thomson Learning (1993)
Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. In: Adaptive Computation and Machine Learning. MIT Press (1998)
Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine Learning 8(3-4), 279–292 (1992), http://jmvidal.cse.sc.edu/library/watkins92a.pdf
Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Communication of the ACM 37(3), 77–84 (1994)
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Clementis, L. (2013). Model Driven Classifier Evaluation in Rule-Based System. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_28
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DOI: https://doi.org/10.1007/978-3-642-32922-7_28
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