Abstract
This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier Systems and Genetic Algorithms. Artificial Intelligence 40, 235–282 (1989)
de Castro, L.N., Timmis, J.I.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)
Dorigo, M., Colombetti, M.: Robot Shaping: An Experiment in Behavior Engineering (Intelligent Robotics and Autonomous Agents). MIT Press, Cambridge (1997)
Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System, Adaptation and Machine Learning. Physica 22D, 187–204 (1986)
Härdle, W.: Applied Nonparametric Regression. Cambridge University Press, Cambridge (1990)
Holland, J.H.: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press, Ann Arbor (1992)
Jerne, N.K.: Towards a Network Theory of the Immune System. Ann. Immunol (Int. Pasteur) 125C, 373–389 (1974)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 1999. LNCS (LNAI), vol. 1813, p. 321. Springer, Heidelberg (2000)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): Advances in Learning Classifier Systems. LNCS (LNAI), vol. 1996. Springer, Berlin (2001)
Michelan, R., Von Zuben, F.J.: Decentralized Control System for Autonomous Navigation based on an Evolved Artificial Immune Network. In: Proc. of the CEC 2002, vol. 2, pp. 1021–1026 (2002)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. The MIT Press, Cambridge (2000)
Watanabe, Y., Ishiguro, A., Uchikawa, H.: Decentralized Behaviour Arbitration Mechanism for Autonomous Mobile Robot Using Immune Network. In: Dasgupta, D. (ed.) Artificial Immune Systems and their Applications, Springer, Heidelberg (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vargas, P.A., de Castro, L.N., Michelan, R., Von Zuben, F.J. (2003). An Immune Learning Classifier Network for Autonomous Navigation. In: Timmis, J., Bentley, P.J., Hart, E. (eds) Artificial Immune Systems. ICARIS 2003. Lecture Notes in Computer Science, vol 2787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45192-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-45192-1_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40766-9
Online ISBN: 978-3-540-45192-1
eBook Packages: Springer Book Archive