Abstract
Wilson’s XCS represents and stores the knowledge it has acquired from an environment as a set of classifiers. In the XCS, don’t cares (#) may be used in the conditions of classifiers to express generalization. This paper is focused on the representation of knowledge with the minimal number of classifiers. For this purpose, a new process called fusion is implemented. Fusion promotes the emergence of more generalized yet accurate classifiers and the reduction of the number of macroclassifiers. Furthermore, to get even more compact rules sets, the implementation of the # symbol in the action of the classifiers is proposed; this allows generalization when possible, and the existence non-competing classifiers in the population if a state has multiple equally correct actions that can be performed. The proposed modified generalized extended XCS (gXCS) was compared with the XCS on the Woods2 environment and a modification of this environment, modified-Woods2, that has locations where there are multiple equally good actions. The performances of XCS and gXCS are very similar; yet, gXCS obtains more parsimonious rule sets. Furthermore, gXCS can find good rule sets even when the probability of # is set zero, contrary to the XCS.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.: Toward a theory of generalization and learning in XCS. IEEE Trans. Evol. Comput. 8(1), 28–46 (2004)
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 253–272. Springer, Heidelberg (2001). doi:10.1007/3-540-44640-0_15
Dorigo, M., Bersini, H.: A comparison of Q-learning and classifier systems. In: Proceedings of From Animals to Animats, Third International Conference on Simulation of Adaptive Behavior, pp. 248–255. MIT Press (1994)
Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge (1986)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. 63, 49 (1977)
Holland, J.H.: Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel rule-bases systems. In: Machine Learning: An Artificial Intelligence Approach, pp. 129–138. Morgan Kauffman (1986)
Nakata, M., Sato, F., Takadama, K.: Towards generalization by identification-based XCS in multi-steps problem. In: 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 389–394 (2011)
Sigaud, O., Wilson, S.W.: Learning classifier systems: a survey. Soft. Comput. 11(11), 1065–1078 (2007)
Watkins, C.C.H., Dayan, P.: Technical note: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)
Wilson, S.W.: Generalization in the XCS classifier system. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 665–674. Morgan Kauffman (1998)
Wilson, S.W.: Knowledge growth in an artificial animal. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 16–23. L. Erlbaum Associates Inc., Hillsdale (1985)
Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)
Wilson, S.W.: State of XCS classifier system research. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 63–81. Springer, Heidelberg (2000). doi:10.1007/3-540-45027-0_3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Garza-Cuéllar, A., Valenzuela-Rendón, M., Parra-Álvarez, RJ. (2017). Towards a More General XCS: Classifier Fusion and Don’t Cares in Actions. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-62428-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62427-3
Online ISBN: 978-3-319-62428-0
eBook Packages: Computer ScienceComputer Science (R0)