Abstract
For effective use in a number of problem domains Learning Classifier Systems must be able to manage multiple objectives. This paper explicitly considers the case of developing the controller for a simulated mobile autonomous robot which must achieve a given task whilst maintaining sufficient battery power. A form of Learning Classifier System in which each rule is represented by an artificial neural network is used. Results are presented which show it is possible to solve both objectives when the energy level is presented as an input along with sensor data. A more realistic, and hence more complex, version of the basic scenario is then investigated.
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References
Ackley, D. & Littman, M. (1992) Interactions Between Learning and Evolution. In C. G. Langton, C. Taylor, J. D. Farmer & S. Rasmussen (eds) Artificial Life II, Addison Wesley, pp487–510.
Ahluwalia, M. & Bull, L. (1999) A Genetic Programming-based Classifier System. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela & R. E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp11–18.
Bull, L. (2001) A Brief Note on the use of Constructivism in Neural Classifier Systems. UWE Learning Classifier Systems Group Technical Report 01-006. Available from http://www.csm.uwe.ac.uk/lcsg.
Bull, L. (2002) On Accuracy-Based Fitness. In L. Bull, P-L. Lanzi & W. Stolzmann (eds) Soft Computing: Special Issue on Learning Classifier Systems 6 (3).
Bull, L. & Hurst, J. (2001) ZCS: Theory and Practice. UWE Learning Classifier Systems Group Technical Report 01-001. To appear in Evolutionary Computation.
Bull, L. & O'Hara, T. (2001) NCS: A Simple Neural Classifier System. UWE Learning Classifier Systems Group Technical Report 01-005.
Butz, M., Goldberg, D. E. & Stolzmann, W. (2000) The Anticipatory Classifier System and Genetic Generalization. IlliGAL Report No. 2000032, University of Illinois at Urban-Champaign, USA.
Cliff, D. & Ross, S. (1994) Adding Temporary Memory to ZCS. Adaptive Behaviour 3:101–150.
Davis, L. (1989) Mapping Neural Networks into Classifier Systems. In J. D. Schaffer (ed) Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, pp375–378.
Deb, K. (2001) Evolutionary Multiobjective Optimization Algorithms. Wiley.
Dorigo, M. & Bersini, H. (1994) A Comparison of Q-learning and Classifier Systems. In D. Cliff, P. Husbands, J-A. Meyer & S. W. Wilson (eds) Proceedings of the Third International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3. MIT Press, pp248–255.
Dorigo, M. & Colombetti, M. (1998) Robot Shaping. MIT Press.
Farmer, J. D. (1989) A Rosetta Stone for Connectionism. Physica D 42:153–187.
Gruau, F. & Whitley, D. (1993) Adding Learning to the Cellular Developmental Process: a Comparative Study. Evolutionary Computation 1(3)
Holland, J. H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press.
Holland, J. H. (1976) Adaptation. In R. Rosen & F. M. Snell (eds) Progress in Theoretical Biology, 4. Plenum.
Holland, J. H. (1986) Escaping Brittleness. In R. S. Michalski, J. G. Carbonell & T. M. Mitchell (eds) Machine Learning: An Artificial Intelligence Approach, 2. Morgan Kauffman, pp48–78.
Hurst, J., Bull, L. & Melhuish, C. (2002) ZCS and TCS Learning Classifier System Controllers on Real Robots. UWE Learning Classifier Systems Group Technical Report 02-002.
Karlsson, J. (1997) Learning to Solve Multiple Goals. PhD Dissertation, Rochester.
Kovacs, T. (2000) Strength or Accuracy? A Comparison of Two Approaches to Fitness Calculation in Learning Classifier Systems. In P-L. Lanzi, W. Stolzmann & S. W. Wilson (eds) Learning Classifier Systems: From Foundations to Applications, Springer, pp194–208.
Lanzi, P-L. & Wilson, S. W. (2001) Toward Optimal Classifier System Performance in Non-Markov Environments. Evolutionary Computation 8(4):393–418.
Moriarty, D. E & Miikulainen, R. (1997) Forming Neural Networks through Efficient and Adaptive Coevolution. Evolutionary Computation 5(2): 373–399.
Schuurmans, D. & Schaeffer, J. (1989) Representational Difficulties with Classifier Systems. In J. D. Schaffer (ed) Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, pp328–333.
Smith, R. E. & Cribbs, B. (1994) Is a Learning Classifier System a Type of Neural Network? Evolutionary Computation 2(1): 19–36.
Valenzuela-Rendon, M. (1991) The Fuzzy Classifier System: a Classifier System for Continuously Varying Variables. In L. Booker & R. Belew (eds) Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, pp346–353.
Watkins, C. (1989) Learning from Delayed Rewards. PhD Dissertation, Cambridge.
Wiering, M. & Schmidhuber, J. (1997) HQ-Learning. Adaptive Behaviour 6(2): 219–246
Wilson, S. W. (1994) ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1):1–18.
Wilson, S. W. (1995) Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2):149–177.
Wilson, S. W. (2000) State of XCS Classifier System Research. In P-L. Lanzi, W. Stolzmann & S. W. Wilson (eds) Learning Classifier Systems: From Foundations to Applications, Springer, pp63–82.
Yao, X. (1999) Evolving Artificial Neural Networks. Proccedings of the IEEE 87(9):1423–1447.
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Bull, L., Studley, M. (2002). Consideration of Multiple Objectives in Neural Learning Classifier Systems. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_53
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DOI: https://doi.org/10.1007/3-540-45712-7_53
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