Abstract
Previous work [1] introduced a new approach to value function approximation in classifier systems called hyperplane coding. Hyperplane coding is a closely related variation of tile coding [13] in which classifier rule conditions fill the role of tiles, and there are few restrictions on the way those “tiles” are organized. Experiments with hyperplane coding have shown that, given a relatively small population of random classifiers, it computes much better approximations than more conventional classifier system methods in which individual rules compute approximations independently. The obvious next step in this line of research is to use the approximation resources available in a random population as a starting point for a more refined approach to approximation that re-allocates resources adaptively to gain greater precision in those regions of the input space where it is needed. This paper shows how to compute such an adaptive function approximation.
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References
Booker, L.B.: Approximating value functions in classifier systems. In: Bull, L., Kovacs, T. (eds.) Foundations of Learning Classifier Systems, Springer, Heidelberg (2005)
Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier Systems and Genetic Algorithms. Artificial Intelligence 40, 235–282 (1989)
Bull, L., O’Hara, T.: Accuracy-based neuro and neuro-fuzzy classifier systems. In: Langdon, W.B., et al. (eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, 9-13 July 2002, pp. 905–911. Morgan Kaufmann, San Francisco (2002)
Butz, M.V., Wilson, S.W.: An Algorithmic Description of XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 253–272. Springer, Heidelberg (2001)
Chen, F., Lambert, D., Pinheiro, J.C.: Incremental quantile estimation for massive tracking. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 516–522. ACM Press, New York (2000)
Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)
Faloutsos, C.: Gray codes for partial match and range queries. IEEE Transactions on Software Engineering 14(10), 1381–1393 (1988)
Hinton, G.E., McClelland, J.L., Rumelhart, D.E.: Distributed representations. In: Rumelhart, D.E., McClelland, J.L., CORPORATE PDP Research Group (eds.) Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations, pp. 77–109. MIT Press, Cambridge (1986)
Iglewicz, B., Hoaglin, D.C.: How to Detect and Handle Outliers. In: American Society for Quality Control Basic References in Quality Control: Statistical Techniques (vol. 16), ASQC Quality Press, Milwaukee (1993)
Miller, W.T., Glanz, F.H., Kraft, L.G.: CMAC: An associative neural network alternative to backpropagation. Proceedings of the IEEE 78(10), 1561–1567 (1990)
Singh, S.P., Jaakkola, T., Jordan, M.I.: Reinforcement learning with soft state aggregation. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 361–368. MIT Press, Cambridge (1995)
Sutton, R.S.: Adapting bias by gradient descent: An incremental version of delta-bar-delta. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 171–176 (1992)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. MIT Press, Cambridge (1998)
Sutton, R.S., Whitehead, S.D.: Online Learning with Random Representations. In: Machine Learning: Proceedings of the Tenth International Conference, pp. 314–321. Morgan Kaufmann, San Mateo (1993)
Venturini, G.: Apprentissage Adaptatif et Apprentissage Supervisé par Algorithme Génétique. PhD thesis, Université de Paris-Sud (1994)
Wilson, S.W.: Classifiers that approximate functions. Natural Computing 1(2-3), 211–234 (2002)
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Booker, L.B. (2007). Adaptive Value Function Approximations in Classifier Systems. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_15
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DOI: https://doi.org/10.1007/978-3-540-71231-2_15
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