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

Autonomous Navigation System Applied To Collective Robotics With Ant-inspired Communication

Research in collective robotics is motivated mainly by the possibility of achieving an efficient solution to multi-objective navigation tasks when multiple robots are employed, instead of a single robot. Several approaches have already been tried in multirobot systems, but the bio-inspired ones are the most frequent. This paper proposes to augment an autonomous navigation system based on learning classifier systems for using in collective robotics, introducing an inter-robot communication mechanism inspired by ant stigmergy, with each robot acting independently and cooperatively. The navigation system has no innate basic behavior and all knowledge necessary to compose the decision-making artifact is evolved as a function of the environmental feedback only, during navigation. Repulsive and/or attractive pheromone trails are produced by the robots along navigation, following very simple rules. Basically, each robot has to perform obstacle avoidance and target search, and the status of the pheromone level at the position currently occupied by each robot will influence the coordination of the two fundamental behaviors. Experiments are performed in simulation, with comparative results indicating that the presence of the pheromone trails is responsible for significant improvements in the capture rate and in the length of the route adopted by each robot. Copyright 2005 ACM.121128Abraham, A., Ramos, V., Web usage mining using artificial ant colony clustering and linear genetic programming (2003) Proceedings of CEC '03, (2), pp. 1384-1391. , Canberra, AustraliaArai, T., Pagello, E., Parker, L., Guest editorial, Advances in multi-robot systems (2002) IEEE Transactions on Robotics and Automation, 18 (5), pp. 655-661Arkin, R.C., (1998) Behavior-based Robotics, , The MIT PressBauer, A., Bullnheimer, B., Hartl, R.F., Strauss, C., An ant colony optimization approach for the single machine total tardiness problem (1999) Proceedings Qf CEC'99, pp. 1445-1450. , Piscataway, USACaetano, F.H., Klaus, J., Zara, F.J., (2002) Ants: Biology and Anatomy, (in Portuguese), , Editera da UNESP, Rio ClaroCamazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-organization in Biological Systems, , Princeton University PressCazangi, R.R., Figueiredo, M.F., Simultaneous emergence of conflicting basic behaviors and their coordination in an evolutionary autonomous navigation system (2002) Proceedings of CEC'02, pp. 466-471. , Honolulu, USACazangi, R.R., Von Zuben, F.J., Figueiredo, M.F., A classifier system in real applications for robot navigation (2003) Proceedings of the CEC'03, (1), pp. 574-580. , Canberra, AustraliaDe Castro, L.N., Von Zuben, F.J., Biologically inspired collective robotics (2004) Recent Developments in Biologically Inspired Computing, , L.N. de Castro & F.J. Von Zuben, Idea Group Inc., Chapter 1França, F.O., Von Zuben, F.J., De Castro, L.N., Definition of capacitated p-medians by a modified max min ant system with local search (2004) Proceedings of 11th International Conference on Neural Information Processing, pp. 1094-1100. , Calcutta, IndiaDing, Y., He, Y., Jiang, J., Multi-robot cooperation method based on the ant algorithm (2003) Proceedings of the 2003 IEEE Swarm Intelligence Symposium, , Indianapolis, USAHolland, J.H., Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems (1986) Machine Learning: An Artificial Intelligence Approach, , Michalsky, R.S., Carbonell, J.G. and Mitchell, T.M., (eds.), Morgan KaufmannKube, C.R., Parker, C.A.C., Wang, T., Zhang, H., Biologically inspired collective robotics (2004) Recent Developments in Biologically Inspired Computing, , L.N. de Castro & F.J. Von Zuben, Idea Group Inc., Chapter 15Sherafat, V., De Castro, L.N., Hruschka, E.R., TermitAnt: An ant clustering algorithm improved by ideas from termite colonies (2004) Proceedings of 11th International Conference on Neural Information Processing, pp. 1088-1093. , Calcutta, IndiaSvennebring, J., Koenig, S., Building terrain-covering ant robots (2002) Technical Report, GIT-COGSCI-2002-10. , College of Computing, Georgia Institute of Technology, Atlanta, USAWagner, I.A., Bruckstein, A.M., Cooperative cleaners: A case of distributed ant-robotics (1997) Communications, Computation, Control, and Signal Processing: A Tribute to Thomas Kailath, pp. 289-308. , Kluwer Academic Publishers, The NetherlandsWatabane, Y., Ishiguro, A., Uchikawa, H., Decentralized behaviour arbitration mechanism for autonomous mobile robot using immune network (1999) Artificial Immune Systems and Their Applications, , D. Dasgupta (Editor), Springe

Evolutionary Stigmergy In Multipurpose Navigation Systems

Autonomous robot navigation involves many challenges and difficulties which are augmented when multiple robots operate together. Sophisticated computational techniques are required to cope with autonomous navigation in collective robotics, being the biologically-inspired approaches the most frequently adopted. Stigmergy, i.e. the ants communication by means of pheromones, is the main biological metaphor used in this work to perform multi-robot communication. The robots will be able to mark regions of the environment with artificial pheromones, according to past experiences, assisting one another in a cooperative and indirect way to accomplish the navigation objectives. Each robot is controlled by an autonomous navigation system (ANS) based on Learning Classifier System, which evolves during navigation from no a priori knowledge. Besides learning to avoid obstacles and capture targets, the systems must also learn how and where to lay artificial pheromones. Some experiments and simulations are performed intending to particularly investigate the ANS from three main perspectives: capability of learning to achieve the navigation objectives in collective scenarios, adaptability in face of environmental changes and ability to obtain optimized navigation behaviors by means of stigmergy. © 2006 IEEE.370377Bonabeau, E., Dorigo, M., Theraulaz, G., (1999) Swarm Intelligence: From Natural to Artificial Systems, , Oxford University PressBalch, T., Arkin, R.C., Communication in reactive multiagent robotic systems (1994) Autonomous Robots, 1 (1), pp. 27-52Grasse, P., La reconstruction du nid et les coordinations inter-individuelle chez bellicoitermes natalenis et cubitermes sp la theorie de la stigmergie: Essai d'interpretation des termites constructeurs (1959) Insectes Sociaux, 6Camazine, S., Franks, N.R., Sneyd, J., Bonabeau, E., Deneubourg, J.-L., Theraula, G., (2001) Self-Organization in Biological Systems, , Princeton University PressHolland, O., Melhuish, C., Stimergy, self-organization, and sorting in collective robotics (1999) Artificial Life, 5 (2), pp. 173-202Wagner, I.A., Bruckstein, A.M., Cooperative cleaners: A study in ant-robotics (1997) Communications, Computation, Control, and Signal Processing, pp. 298-308Ding, Y., He, Y., Jiang, J., Multi-robot cooperation method based on. the ant algorithm (2003) Proc. of the 2003 IEEE Swarm Intelligence Symposium, pp. 14-18. , Indianapolis, USA: IEEEVaughan, R., Stoy, K., Sukhatme, G., Mataric, M., Lost: Localization-space trails for robot teams (2002) IEEE Transactions on Robotics and Automation, 18 (5), pp. 796-812Sauter, J., Matthews, R., Parunak, H., Brueckner, S., Evolving adaptive pheromone path planning mechanisms (2002) Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 434-440Wurr, A., Robotic team navigation in complex environments using stigmergic clues, (2003), Master's thesis, University of ManitobaCazangi, R.R., Zuben, F.J.V., Figueiredo, M.F., Autonomous navigation system applied to collective robotics with ant-inspired communication (2005) Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, 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 KaufmannSvennebring, J., Koenig, S., Towards building terrain-covering ant robots (2002) Ant Algorithms, pp. 202-215Cazangi, R.R., Figueiredo, M., Simultaneous emergence of conflicting basic behaviors and their coordination in an evolutionary autonomous navigation system (2002) Proceedings of the 2002 Congress on Evolutionary Computation, pp. 466-471. , Honolulu, USACazangi, R.R., Zuben, F.J.V., Figueiredo, M.F., A classifier system in real applications for robot navigation (2003) Proceedings of the 2003 Congress on Evolutionary Computation, 1, pp. 574-580. , Canberra, Australia: IEEE PressZiemke, T., On the role of robot simulations in embodied cognitive science (2003) Artificial Intelligence and Simulation of Behaviour, 1 (4), pp. 1-11Reinelt, G., (1990) TSPLIB - A t.s.p. library, , Universität Augsburg, Institut für Mathematik, Augsburg, Tech. Rep. 25

A Classifier System In Real Applications For Robot Navigation

This paper presents an autonomous evolutionary system applied to control a mobile robot in unknown environments. The navigation system learns efficiently to deal with situations where the robot must capture targets avoiding collisions with obstacles. Toward this end, robot direction and speed must be properly defined. The evolutionary approach is based on a version of classifier systems, responsible for the proposition of a competitive process involving rules of elementary behaviour. A virtual environment is used to evolve the controller, a Khepera II robot is submitted to real navigation tasks, with no significant degradation in performance. As an additional experiment, the controller is also evolved in a real environment, and validated in a different and more complex environment, not previously experimented, attesting the generalization capability of the proposal. © 2003 IEEE.1574580Arkin, R.C., (1998) Behavior-based Robotics, , The MIT PressCazangi, R.R., Figueiredo, M.F., Simultaneous emergence of conflicting basic behaviors and their coordination in an evolutionary autonomous navigation system (2002) Proceedings of the 2002 Congress on Evolutionary Computation (CEC'2002), pp. 466-471. , Honolulu, EUACrestani Jr., P.R., Von Zuben, F.J., Figueiredo, M.F., A hierarchical neuro-fuzzy approach to autonomous navigation (2002) Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN'2002), pp. 2339-2344. , Honolulu, EUAFigueiredo, M., Gomide, F., Evolving neurofuzzy networks for basic behaviors and a recategorization approach for their'coordination (1996) Genetic Algorithms and Soft Computing, pp. 533-552. , Herrera, F. and Verdegay, J. eds, Physica-VerlagFloreano, D., Mondada, F., Hardware solutions for evolutionary robotics (1998) Proceedings of the I European Workshop of Evolutionary Robotics, pp. 137-151. , P. Husbands and J-A. Meyer, Springer-VerlagFogel;, D.B., (1999) Evolutionary Computation - Toward a New Philosophy of Machine Intelligence, , 2nd edition, IEEE PressGrefenstette, J.J., A system for learning control strategies with genetic algorithms (1989) Proceedings of the III ICGA, Morgan Kaufmann, pp. 183-190Holland, J.H., Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems (1986) Machine Learning: An Artificial Intelligence Approach, pp. 593-623. , Michalsky, R. S., Carbonell, J. G. and Mitchell, T. M., eds., Morgan Kaufmannhttp://www.kteam.com/robots/kheperaLanzi, P.L., Stolzmann, W., Wilson, S.W., Learning classifier systems. from foundations to applications (2000) Lecture Notes in Artificial Intelligence, 1813. , Springer-VerlagMondada, F., Franzi, E., Jenne, P., Mobile robot miniaturisation: A tool for investigating in control algorithms (1993) Proceedings of the III International Symposium on Experimental Robotics, pp. 501-513. , T. Yoshikawa and F. Miyazaki, eds., Springer-Verlag, BerlinMondada, F., Verschure, P.F.M.J., Modeling system-environment interaction: The complementary roles of simulations and real world artifacts (1993) Proceedings of the II European Conference on Artificial Life (ECAL'93), pp. 808-817. , BrusselsPfeifer, R., Scheier, C., (1999) Understanding Intelligence, , MLT PressRichards, R.A., (1995) Zero, H-order Shape Optimization Utilizing Learning Classifier Systems, , PhD Thesis, Stanford UniversitySteels, L., When are robots intelligent autonomous agents? (1995) Journal of Robotics and Autonomous Systems, 15, pp. 3-9Verschure, P.F.M.J., A bottom up approach towards the acquisition and expression of sequential representations applied to a behaving real-world device: Distributed adaptive control III (1998) Neural Networks, 11, pp. 1531-1549Wilson, M., King, C., Hunt, J., Evolving hierarchical robot behaviors (1997) Robotics and Autonomous Systems, 22 (3-4), pp. 215-23