Abstract
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of an accuracy-based scheme where fitness is based on a rule’s ability to predict the expected payoff from its use. Learning Classifier Systems that build anticipations of the expected states following their actions are also a focus of current research. This paper presents a simple but effective learning classifier system of this last type, using payoff-based fitness, with the aim of enabling the exploration of their basic principles, i.e., in isolation from the many other mechanisms they usually contain. The system is described and modelled, before being implemented. Comparisons to an equivalent accuracy-based system show similar performance. The use of self-adaptive mutation in such systems in general is then considered.
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.
References
Back, T.: Self-Adaptation in Genetic Algorithms. In: Varela, F.J., Bourgine, P. (eds.) Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pp. 263–271. MIT Press, Cambridge (1992)
Booker, L.B.: Triggered Rule Discovery in Classifier Systems. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 265–274. Morgan Kaufmann, San Francisco (1989)
Bull, L.: Simple Markov Models of the Genetic Algorithm in Classifier Systems. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 29–36. Springer, Heidelberg (2001)
Bull, L.: Lookahead and Latent Learning in ZCS. In: Langdon, W.B., Cantu-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 897–904. Morgan Kaufmann, San Francisco (2002)
Bull, L.: Lookahead and Latent Learning in a Simple Accuracy-based Learning Classifier System. In: Yao, X., et al. (eds.) Parallel Problem Solving from Nature - PPSN VIII, pp. 1042–1050. Springer, Heidelberg (2004)
Bull, L.: Two Simple Learning Classifier Systems. In: Bull, L., Kovacs, T. (eds.) Foundations of Learning Classifier Systems, pp. 63–90. Springer, Heidelberg (2005)
Bull, L., Hurst, J.: ZCS Redux. Evolutionary Computation 10(2), 185–205 (2002)
Bull, L., Hurst, J.: Lookahead and Latent Learning in a Neural Learning Classifier System with Self-Adaptive Constructivism. Technical Report UWELCSG03-012 (2003), http://www.cems.uwe.ac.uk/lcsg
Bull, L., Studley, M.: Consideration of Multiple Objectives in Neural Learning Classifier Systems. In: Merelo, J., Adamidis, P., Beyer, H.-G., FernandezVillicanas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002, vol. 2439, pp. 558–567. Springer, Heidelberg (2002)
Bull, L., Hurst, J., Tomlinson, A.: Self-Adaptive Mutation in Classifier System Controllers. In: Meyer, J.-A., Berthoz, A., Floreano, D., Roitblatt, H., Wilson, S.W. (eds.) From Animals to Animats 6 - The Sixth International Conference on the Simulation of Adaptive Behaviour, pp. 460–470. MIT Press, Cambridge (2000)
Bull, L., Lawson, I., Adamatzky, A., De Lacy Costello, B.: Towards Predicting Spatial Complexity: A Learning Classifier System Approach to Cellular Automata Identification. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 136–141. IEEE, Los Alamitos (2005)
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)
Butz, M.V., Stolzmann, W.: An Algorithmic Description of ACS2. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 211–230. Springer, Heidelberg (2002)
Butz, M.V., Goldberg, D.E., Tharakunnel, K.: Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy. Evolutionary Computation 11(3), 239–278 (2003)
Dorigo, M., Colombetti, M.: Robot Shaping. MIT Press, Cambridge (1997)
Gerard, P., Sigaud, O.: YACS: Combining Dynamic Programming with Generalization in Classifier Systems. In: Lanzi, P.-L., Stolzmann, W., Wilson, S.W. (eds.) Advances in Learning Classifier Systems: Proceedings of the Third International Workshop, pp. 52–69. Springer, Heidelberg (2001)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Holland, J.H.: Adaptation. In: Rosen, R., Snell, F.M. (eds.) Progress in Theoretical Biology, vol. 4, pp. 263–293. Plenum (1976)
Holland, J.H.: Properties of the Bucket Brigade. In: Grefenstette, J.J. (ed.) Proceedings of the First International Conference on Genetic Algorithms and their Applications, pp. 1–7. Lawrence Erlbaum Associates, Mahwah (1985)
Holland, J.H.: Escaping Brittleness. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, vol. 2, pp. 48–78. Morgan Kauffman, San Francisco (1986)
Holland, J.H.: Concerning the Emergence of Tag-Mediated Lookahead in Classifier Systems. Physica D 42, 188–201 (1990)
Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction: Processes of Inference, Learning and Discovery. MIT Press, Cambridge (1986)
Hurst, J., Bull, L.: A Self-Adaptive Classifier System. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 70–79. Springer, Heidelberg (2001)
Hurst, J., Bull, L.: A Self-Adaptive XCS. In: Lanzi, P.-L., Stolzmann, W., Wilson, S.W. (eds.) Advances in Learning Classifier Systems: Proceedings of the Fourth International Workshop on Learning Classifier Systems, pp. 57–73. Springer, Heidelberg (2002)
O’Hara, T., Bull, L.: Building Anticipations in an Accuracy-based Learning Classifier System by use of an Artificial Neural Network. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2046–2052. IEEE Press, Los Alamitos (2005)
Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog (1973)
Riolo, R.: Lookahead Planning and Latent Learning in a Classifier System. In: Meyer, J.-A., Wilson, S.W. (eds.) From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behaviour, pp. 316–326. MIT Press, Cambridge (1991)
Seward, J.P.: An Experimental Analysis of Latent Learning. Journal of Experimental Psychology 39, 177–186 (1949)
Stolzmann, W.: Anticipatory Classifier Systems. In: Koza, J.R. (ed.) Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 658–664. Morgan Kaufmann, San Francisco (1998)
Wilson, S.W.: ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1), 1–18 (1994)
Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–177 (1995)
Wyatt, D., Bull, L.: A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces. In: Krasnagor, N., Hart, W., Smith, J. (eds.) Recent Advances in Memetic Algorithms, pp. 355–396. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bull, L. (2008). On Lookahead and Latent Learning in Simple LCS. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-88138-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88137-7
Online ISBN: 978-3-540-88138-4
eBook Packages: Computer ScienceComputer Science (R0)