Immune Learning Classifier Networks: Evolving Nodes And Connections

The design of an autonomous navigation system with multiple tasks to be accomplished in unknown environments represents a complex undertaking. With the simultaneous purposes of capturing targets and avoiding obstacles, the challenge may become still more intricate if the configuration of obstacles and targets creates local minima, like concave shapes and mazes between the robot and the target. Pure reactive navigation systems are not able to deal properly with such hampering scenarios, requiring additional cognitive apparatus. Concepts from immune network theory are then employed to convert an earlier reactive robot controller, based on learning classifier systems, into a connectionist device. Starting from no a priori knowledge, both the classifiers and their connections are evolved during the robot navigation. Some experiments with and without local minima are carried out and the proposed evolutionary network of classifiers was shown to produce connectionist navigation systems capable of successfully overcoming local minima. © 2006 IEEE.22302237Deb, K., (2001) Multi-Objective Optimization Using Evolutionary Algorithms, , Chichester, UK: WileyRam, A., Arkin, R.C., Moorman, K., Clark, R.J., Case-based reactive navigation: A method for on-line selection and adaptation of reactive robotic control parameters (1997) IEEE Trans, on Systems, Man, and Cybernetics, Part B, 27 (3), pp. 376-394Krishna, K.M., Kalra, P.K., Solving the local minima problem for a mobile robot by classification, of spatio-temporal sensory sequences (2000) Journal of Robotic Systems, 17, pp. 549-564. , OctFodor, J.A., Pylyshyn, Z.W., Connectionism and cognitive architecture: A critical analysis (1988) Cognition, 28, pp. 3-72P. Smolensky, On the proper treatment of connectionism, University of Colorado, Dept. of Computer Science, Boulder, CO, Tech. Rep. CU-CS-377-87, 1987Farmer, J., A rosetta stone for connectionism (1990) Physica D, 42 (1-3), pp. 153-187Bates, E., Elman, J., Connectionism. and the study of change (2002) Brain development and cognition: A reader, , 2nd ed, M. Johnson, Ed. Oxford: Blackwell PublishersCazangi, R.R., Von Zuben, F.J., Figueiredo, M.F., A. classifier system in real applications for robot navigation (2003) Proc. of the 2003 CEC, 1, pp. 574-580. , Canberra, Australia: IEEE PressCazangi, R.R., Von Zuben, F.J., Figueiredo, M.F., Autonomous navigation, system applied to collective robotics with ant-inspired communication (2005) Proc. of the 2005 GECCO, 1, pp. 121-128. , Washington DC, USA: ACM Press_, Stigmergic autonomous navigation in collective robotics, in Stigmergic Optimization, A. Abraham, C. Grosan, and V. Ramos, Eds. Springer-Verlag, 2006Holland, J., Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems (1986) Machine Intelligence II, , R. Michalsky, J. Carbonell, and T. Mitchell, Eds. Morgan KaufmannHershberg, U., Efroni, S., The immune system, and other cognitive systems (2001) Complexity, 6 (5), pp. 14-21de Castro, L.N., Immune cognition, micro-evolution, and a personal account on immune engineering (2003) S.E.E.D. Journal, 3 (3), pp. 134-155Jerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol, 125 C, pp. 373-389Farmer, J., Packard, N., Perelson, A., The immune system, adaptation, and machine learning (1986) Physica, 22 D, pp. 187-204de Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Paradigm, , Springer-VerlagLumelsky, V., A comparative study on the path length performance of maze-searching and robot motion planning algorithms (1991) IEEE Trans. on Robotics and Automation, 7 (1), pp. 57-66Kube, C., Parker, C., Wang, T., Zhang, H., Biologically inspired collective robotics (2004) Recent Developments in. Biologically Inspired Computing, , L. de Castto and F. Von Zuben, Eds. Idea GroupNolfi, S., Floriano, D., (2000) Evolutionary Robotics, , MIT PressParisi, D., Calabretta, R., (2001) Evolutionary connectionism. and mind/brain modularity, , Institute of Psychology, National Research CouncilRome, Italy, Tech. Rep. NSAL 01-01Kim, K.-J., Yoo, J.-O., Cho, S.-B., Robust inference of bayesian networks using speciated evolution and ensemble (2005) ISMIS, pp. 92-101Nolfi, S., Floreano, D., Miglino, O., Mondada, F., How to evolve autonomous robots: Different approaches in evolutionary robotics (1994) Proc. of the 4th International Workshop on the Synthesis and Simulation of Living Systems ArtificialLifeIV, pp. 190-197Vasilyev, A., Autonomous agent control using connectionist XCS classifier system (2002) Transport and Telecommunication, 3 (3), pp. 56-63P. A, Vargas, L. N. de Castro, R. Michelan, and F. J. Von. Zuben, An immune learning classifier network for autonomous navigation, in Proc. of the Second ICARIS, 2003, pp. 69-8