Self-adaptive constructivism in Neural XCS and XCSF

ABSTRACT

For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility guided by the environment at any given time. This paper presents the use of constructivism-inspired mechanisms within a neural learning classifier system which exploits parameter self-adaptation as an approach to realize such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the system. Further, the use of computed predictions is shown possible.

REFERENCES

• 1: Ahluwalia, M. & Bull, L. 1999. A Genetic Programming Classifier System. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-99. San Mateo, CA: Morgan Kaufmann, pp11--18.
• 2: Larry Bull, On Using Constructivism in Neural Classifier Systems, Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, p.558-567, September 07-11, 2002
• 3: Larry Bull , Toby O'Hara, Accuracy-based Neuro And Neuro-fuzzy Classifier Systems, Proceedings of the Genetic and Evolutionary Computation Conference, p.905-911, July 09-13, 2002
• 4: Bull, L., Hurst, J., & Tomlinson, A. 2000. Self-Adaptive Mutation in Classifier System Controllers. In J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblatt & S.W. Wilson (Eds.) From Animals to Animats 6 - The Sixth International Conference on the Simulation of Adaptive Behaviour, MIT Press.
• 5: Martin Butz , Stewart W. Wilson, An Algorithmic Description of XCS, Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems, p.253-272, September 15-16, 2000
• 6: Cordón, O., Herrera, F., Hoffmann, F. & Magdalena, L. 2001. Genetic Fuzzy Systems. Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. World Scientific.
• 7: Edelman, G. 1987. Neural Darwinism: The Theory of Neuronal Group Selection. New York: Basic Books.
• 8: Inman Harvey , Phil Husbands , Dave Cliff, Seeing the light: artificial evolution, real vision, Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3, p.392-401, July 1994, Brighton, United Kingdom
• 9: John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
• 10: Holland, J.H. 1976. Adaptation. In R. Rosen & F.M. Snell (Eds.) Progress in Theoretical Biology 4. New York: Academic Press, pp263--293.
• 11: Jacob Hurst , Larry Bull, A Neural Learning Classifier System with Self-Adaptive Constructivism for Mobile Robot Control, Artificial Life, v.12 n.3, p.353-380, July 2006 [doi>10.1162/artl.2006.12.3.353]
• 12: Pier Luca Lanzi, An analysis of generalization in the xcs classifier system, Evolutionary Computation, v.7 n.2, p.125-149, Summer 1999 [doi>10.1162/evco.1999.7.2.125]
• 13: Pier Luca Lanzi , Daniele Loiacono, Classifier systems that compute action mappings, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England [doi>10.1145/1276958.1277322]
• 14: Quartz, S.R. & Sejnowski, T.J. 1997. The Neural Basis of Cognitive Development: A Constructionist Manifesto. Behavioural and Brain Sciences 20(4): 537--596.
• 15: David E. Rumelhart , James L. McClelland , CORPORATE PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations, MIT Press, Cambridge, MA, 1986
• 16: Hans-Paul Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons, Inc., New York, NY, 1981
• 17: Matthew Studley , Larry Bull, Using the XCS Classifier System for Multi-objective Reinforcement Learning Problems, Artificial Life, v.13 n.1, p.69-86, Winter 2007 [doi>10.1162/artl.2007.13.1.69]
• 18: Stewart W. Wilson, Zcs: A zeroth level classifier system, Evolutionary Computation, v.2 n.1, p.1-18, Spring 1994 [doi>10.1162/evco.1994.2.1.1]
• 19: Stewart W. Wilson, Classifier fitness based on accuracy, Evolutionary Computation, v.3 n.2, p.149-175, Summer 1995 [doi>10.1162/evco.1995.3.2.149]
• 20: Wilson, S.W. 2001. Function Approximation with a Classifier System. In Spector, L., D., G. E., Wu, A., Langdon, W.B., Voight, H. M., and Gen, M., (Eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 01) Morgan Kaufmann. pp 974--981
• 21: Wilson, S.W. 2007. Three architectures for continuous action Learning Classifier Systems. International Workshops, IWLCS 2003-2005, Revised Selected Papers. In T. Kovacs, X. Llorà, K. Takadama, P. L. Lanzi, W. Stolzmann, S. W. Wilson (Eds.) Lecture Notes in Artificial Intelligence (LNAI-4399),. Berlin, Springer-Verlag. pp. 239--257

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets; Parameter learning

General Terms:
Experimentation

Keywords:
constructivism, learning classifier systems, neural networks, reinforcement learning, self-adaptation