research-article

Self-adaptive constructivism in Neural XCS and XCSF

Authors:

Gerard D. Howard,

Pier-Luca LanziAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1389 - 1396

https://doi.org/10.1145/1389095.1389364

Published: 12 July 2008 Publication History

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]

Bull, L. 2002. On Using Constructivism in Neural Classifier Systems. In Merelo, J, Adamidis, P., Beyer, H-G., Fernandez-Villacanas, J-L., & Schwefel, H-P. (Eds.) Parallel Problem Solving from Nature - PPSN VII. Springer Verlag, pp558--567.]]

Digital Library

[3]

Bull, L. & O'Hara, T. 2002. Accuracy-based Neuro and Neuro-Fuzzy Classifier Systems. In W.B. Langdon, E.Cantu-Paz, K. Mathias, R.Roy, D.Davis, R.Poli, K. Balakrishnan, V. Hanavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F.Miller, E.Burke & N. Jonoska (Eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann. pp905--911.]]

[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]

Butz, M. V., & Wilson, S. W. 2001. An Algorithmic Description of XCS. In Lanzi, P. L., Stolzmann, W., and S. W. Wilson (Eds.), Advances in Learning Classifier Systems, LNAI 1996, pp. 253--272. Berlin: Springer-Verlag]]

Digital Library

[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]

Harvey, I., Husbands, P. & Cliff, D. 1994. Seeing the Light: Artificial Evolution, Real Vision. In D. Cliff, P. Husbands, JA. Meyer & S.W. Wilson (eds) From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour. Cambridge, MA: MIT Press, pp392--401.]]

Digital Library

[9]

Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.]]

Digital Library

[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]

Hurst, J. & Bull, L. 2006. A Neural Learning Classifier System with Self-Adaptive Constructivism for Mobile Robot Control. Artificial Life 12(3): 353 -- 380]]

Digital Library

[12]

Lanzi, P.L. 1999. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation 7(2): 125--149]]

Digital Library

[13]

Lanzi, P.L. & Loiacono, D. 2007. Classifier systems that compute action mappings. In GECCO '07: Proceedings of the 9th annual Conference on Genetic and Evolutionary Computation, New York, NY, USA. ACM Press. pp1822--1829.]]

Digital Library

[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]

Rumelhart, D.E. & McClelland, J.L. 1986. Parallel Distributed Processing. Cambridge, MA: MIT Press]]

Digital Library

[16]

Schwefel, H-P. 1981. Numerical Optimization of Computer Models. Wiley, Chichester,]]

Digital Library

[17]

Studley, M. & Bull, L. 2006. Using the XCS Classifier System for Multi-objective Reinforcement Learning Problems. Artificial Life 13(1): 69--86.]]

Digital Library

[18]

Wilson, S.W. 1994. ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1):1--18.]]

Digital Library

[19]

Wilson, S.W. 1995. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149--175.]]

Digital Library

[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]]

Digital Library

Cited By

Lanzi PLoiacono D(2015)XCSF with tile coding in discontinuous action-value landscapesEvolutionary Intelligence10.1007/s12065-015-0129-78:2-3(117-132)Online publication date: 11-Apr-2015
https://doi.org/10.1007/s12065-015-0129-7
Urbanowicz RGranizo-Mackenzie AMoore J(2012)An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systemsIEEE Computational Intelligence Magazine10.1109/MCI.2012.22151247:4(35-45)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/MCI.2012.2215124
Kovacs T(2012)Genetics-Based Machine LearningHandbook of Natural Computing10.1007/978-3-540-92910-9_30(937-986)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_30
Show More Cited By

Index Terms

Self-adaptive constructivism in Neural XCS and XCSF
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Lanzi PLoiacono D(2015)XCSF with tile coding in discontinuous action-value landscapesEvolutionary Intelligence10.1007/s12065-015-0129-78:2-3(117-132)Online publication date: 11-Apr-2015
https://doi.org/10.1007/s12065-015-0129-7
Urbanowicz RGranizo-Mackenzie AMoore J(2012)An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systemsIEEE Computational Intelligence Magazine10.1109/MCI.2012.22151247:4(35-45)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/MCI.2012.2215124
Kovacs T(2012)Genetics-Based Machine LearningHandbook of Natural Computing10.1007/978-3-540-92910-9_30(937-986)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_30
Preen RBull LRothlauf F(2009)Discrete dynamical genetic programming in XCSProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570075(1299-1306)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570075
Howard GBull LLanzi PRothlauf F(2009)Towards continuous actions in continuous space and time using self-adaptive constructivism in neural XCSFProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570065(1219-1226)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570065
Kohl NMiikkulainen R(2009)2009 Special IssueNeural Networks10.1016/j.neunet.2009.03.00122:3(326-337)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1016/j.neunet.2009.03.001
Howard GBull LRyan C(2008)On the effects of node duplication and connection-oriented constructivism in neural XCSFProceedings of the 10th annual conference companion on Genetic and evolutionary computation10.1145/1388969.1389010(1977-1984)Online publication date: 12-Jul-2008
https://dl.acm.org/doi/10.1145/1388969.1389010

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