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A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999)

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Learning Classifier Systems (IWLCS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1813))

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Abstract

In 1989 Wilson and Goldberg presented a critical review of the first ten years of learning classifier system research. With this paper we review the subsequent ten years of learning classifier systems research, discussing the main achievements and the major research directions pursued in those years.

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References

  1. Manu Ahluwalia and Larry Bull. A Genetic Programming-based Classifier System. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 11–18.

    Google Scholar 

  2. W. Brian Arthur, John H. Holland, Blake LeBaron, Richard Palmer, and Paul Talyer. Asset Pricing Under Endogenous Expectations in an Artificial Stock Market. Technical report, Santa Fe Institute, 1996. This is the original version of LeBaron1999a.

    Google Scholar 

  3. Thomas Bäck, editor. Proceedings of the 7th International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann: San Francisco CA, 1997.

    Google Scholar 

  4. Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999.

    Google Scholar 

  5. Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 1 — Effects. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 19–26.

    Google Scholar 

  6. Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 2 — Solutions. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 27–34.

    Google Scholar 

  7. Andrea Bonarini. An Introduction to Learning Fuzzy Classifier Systems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 83–104. (this volume).

    Google Scholar 

  8. Pierre Bonelli and Alexandre Parodi. An Efficient Classifier System and its Experimental Comparison with two Representative learning methods on three medical domains. In Richard K. Belew, editors. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91). Morgan Kaufmann: San Francisco CA, July 1991 Booker and Belew [15], pages 288–295.

    Google Scholar 

  9. Pierre Bonelli, Alexandre Parodi, Sandip Sen, and Stewart W. Wilson. NEW-BOOLE: A Fast GBML System. In International Conference on Machine Learning, pages 153–159, San Mateo, California, 1990. Morgan Kaufmann.

    Google Scholar 

  10. Lashon B. Booker. Classifier Systems that Learn Internal World Models. Machine Learning, 3:161–192, 1988.

    Google Scholar 

  11. Lashon B. Booker. Triggered rule discovery in classifier systems. In Schaffer [125], pages 265–274.

    Google Scholar 

  12. Lashon B. Booker. Instinct as an Inductive Bias for Learning Behavioral Sequences. In S. W. Wilson, editors. From Animals to Animats 1. Proceedings of the First International Conference on Simulation of Adaptive Behavior (SAB90). A Bradford Book. MIT Press, 1990 Meyer and Wilson [99], pages 230–237.

    Google Scholar 

  13. Lashon B. Booker. Representing Attribute-Based Concepts in a Classifier System. In Rawlins [105], pages 115–127.

    Google Scholar 

  14. Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 125–142. (this volume).

    Google Scholar 

  15. Lashon B. Booker and Richard K. Belew, editors. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91). Morgan Kaufmann: San Francisco CA, July 1991.

    Google Scholar 

  16. Larry Bull. On using ZCS in a Simulated Continuous Double-Auction Market. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 83–90.

    Google Scholar 

  17. Y. J. Cao, N. Ireson, Larry Bull, and R. Miles. Design of a Traffic Junction Controller using a Classifier System and Fuzzy Logic. In Proceedings of the Sixth International Conference on Computational Intelligence, Theory, and Applications. Springer-Verlag, 1999.

    Google Scholar 

  18. P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4):261–283, 1989.

    Google Scholar 

  19. Dave Cliff, Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB94). A Bradford Book. MIT Press, 1994.

    Google Scholar 

  20. Dave Cliff and Susi Ross. Adding Temporary Memory to ZCS. Adaptive Behavior, 3(2):101–150, 1995. Also technical report: ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp347.ps.Z.

    Article  Google Scholar 

  21. Philippe Collard and Cathy Escazut. Relational Schemata: A Way to Improve the Expressiveness of Classifiers. pages 397–404. Morgan kaufmann Publishers: San Francisco CA, 1995.

    Google Scholar 

  22. Marco Colombetti and Marco Dorigo. Training agents to perform sequential behavior. Adaptive Behavior, 2(3):247–275, 1994. ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.06-ADAP94.ps.gz.

    Article  Google Scholar 

  23. Marco Colombetti and Marco Dorigo. Evolutionary Computation in Behavior Engineering. In Evolutionary Computation: Theory and Applications, chapter 2, pages 37–80. World Scientific Publishing Co.: Singapore, 1999. Also Tech. Report. TR/IRIDIA/1996-1, IRIDIA, Université Libre de Bruxelles.

    Google Scholar 

  24. Marco Colombetti, Marco Dorigo, and G. Borghi. Behavior Analysis and Training: A Methodology for Behavior Engineering. IEEE Transactions on Systems, Man and Cybernetics, 26(6):365–380, 1996.

    Google Scholar 

  25. Marco Colombetti, Marco Dorigo, and G. Borghi. Robot shaping: The HAMSTER Experiment. In M. Jamshidi et al., editor, Proceedings of ISRAM’96, Sixth International Symposium on Robotics and Manufacturing, May 28–30, Montpellier, France, 1996.

    Google Scholar 

  26. Michael Sean Davis. A Computational Model of Affect Theory: Simulations of Reducer/Augmenter and Learned Helplessness Phenomena. PhD thesis, Department of Psychology, University of Michigan, 2000.

    Google Scholar 

  27. T.G. Dietterich. Hierarchical reinforcement learning with the maxq value function decomposition. Submitted for journal publication. Available at.

    Google Scholar 

  28. Jean-Yves Donnart and Jean-Arcady Meyer. A hierarchical classifier system implementing a motivationally autonomous animat. In Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB94). A Bradford Book. MIT Press, 1994 Cliff et al. [19], pages 144–153.

    Google Scholar 

  29. Jean-Yves Donnart and Jean-Arcady Meyer. Hierarchical-map Building and Self-positioning with MonaLysa. Adaptive Behavior, 5(1):29–74, 1996.

    Article  Google Scholar 

  30. Jean-Yves Donnart and Jean-Arcady Meyer. Spatial Exploration, Map Learning, and Self-Positioning with MonaLysa. In Maja J. Mataric, Jean-Arcady Meyer, Jordan Pollack, and Stewart W. Wilson, editors. From Animals to Animats 4. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB96). A Bradford Book. MIT Press, 1996 Maes et al. [97], pages 204–213.

    Google Scholar 

  31. Marco Dorigo. Using Transputers to Increase Speed and Flexibility of Genetic-based Machine Learning Systems. Microprocessing and Microprogramming, 34:147–152, 1991.

    Article  Google Scholar 

  32. Marco Dorigo. Genetic and Non-Genetic Operators in ALECSYS. Evolutionary Computation, 1(2):151–164, 1993. Also Tech. Report TR-92-075 International Computer Science Institute.

    Article  MathSciNet  Google Scholar 

  33. Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning, 19:209–240, 1995.

    Google Scholar 

  34. Marco Dorigo and Hugues Bersini. A Comparison of Q-Learning and Classifier Systems. In Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB94). A Bradford Book. MIT Press, 1994 Cliff et al. [19], pages 248–255.

    Google Scholar 

  35. Marco Dorigo and Marco Colombetti. Robot shaping: Developing autonomous agents through learning. Artificial Intelligence, 2:321–370, 1994. ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.05-AIJ94.ps.gz.

    Article  Google Scholar 

  36. Marco Dorigo and Marco Colombetti. Robot Shaping: An Experiment in Behavior Engineering. MIT Press/Bradford Books, 1998.

    Google Scholar 

  37. Marco Dorigo and U. Schnepf. Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis. IEEE Transactions on Systems, Man and Cybernetics, 23(1):141–154, 1993.

    Article  Google Scholar 

  38. Marco Dorigo and Enrico Sirtori. Alecsys: A Parallel Laboratory for Learning Classifier Systems. In Richard K. Belew, editors. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91). Morgan Kaufmann: San Francisco CA, July 1991 Booker and Belew [15], pages 296–302.

    Google Scholar 

  39. Barry B. Druhan and Robert C. Mathews. THIYOS: A Classifier System Model of Implicit Knowledge in Artificial Grammars. In Proc. Ann. Cog. Sci. Soc., 1989.

    Google Scholar 

  40. Cathy Escazut and Terence C. Fogarty. Coevolving Classifier Systems to Control Traffic Signals. In John R. Koza, editor, Late Breaking Papers at the 1997 Genetic Programming Conference, Stanford University, CA, USA, July 1997. Stanford Bookstore.

    Google Scholar 

  41. Francine Federman and Susan Fife Dorchak. Information Theory and NEXT-PITCH: A Learning Classifier System. In Bäck [3], pages 442–449.

    Google Scholar 

  42. Francine Federman and Susan Fife Dorchak. Representation of Music in a Learning Classifier System. In Rad and Skowron, editors, Foundations of Intelligent Systems: Proceedings 10th International Symposium (ISMIS’97). Springer-Verlag: Heidelberg, 1997.

    Google Scholar 

  43. Francine Federman and Susan Fife Dorchak. A Study of Classifier Length and Population Size. In Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998 Koza et al. [83], pages 629–634.

    Google Scholar 

  44. Stephanie Forrest, editor. Proceedings of the 5th International Conference on Genetic Algorithms (ICGA93). Morgan Kaufmann, 1993.

    Google Scholar 

  45. Francine Federman and Gayle Sparkman and Stephanie Watt. Representation of Music in a Learning Classifier System Utilizing Bach Chorales. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], page 785. One page poster paper.

    Google Scholar 

  46. Peter W. Frey and David J. Slate. Letter Recognition Using Holland-Style Adaptive Classifiers. Machine Learning, 6:161–182, 1991.

    Google Scholar 

  47. A. H. Gilbert, Frances Bell, and Christine L. Valenzuela. Adaptive Learning of Process Control and Profit Optimisation using a Classifier System. Evolutionary Computation, 3(2):177–198, 1995.

    Article  Google Scholar 

  48. D. E. Goldberg. Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3:493–530, 1989.

    MATH  MathSciNet  Google Scholar 

  49. David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.

    MATH  Google Scholar 

  50. D.E. Goldberg and J. Richardson. Genetic Algorithms with Sharing for Multimodal Function Optimization. In Proceedings of the Second International Conference on Genetic Algorithms, pages 41–49, 1987.

    Google Scholar 

  51. John J. Grefenstette, editor. Proceedings of the 1st International Conference on Genetic Algorithms and their Applications (ICGA85). Lawrence Erlbaum Associates: Pittsburgh, PA, July 1985.

    Google Scholar 

  52. John J. Grefenstette. Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms. Machine Learning, 3:225–245, 1988.

    Google Scholar 

  53. Adrian Hartley. Genetics Based Machine Learning as a Model of Perceptual Category Learning in Humans. Master’s thesis, University of Birmingham, 1998. ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/index.html.

  54. Adrian Hartley. Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 266–273.

    Google Scholar 

  55. R. Hayes-Roth. Patterns of induction and associated knowledge acquisition algorithms. In Pattern Recognition and artificial intelligence. New York: Academic Press, 1976.

    Google Scholar 

  56. John H. Holland. Processing and processors for schemata. In E. L. Jacks, editor, Associative information processing, pages 127–146. New York: American Elsevier, 1971.

    Google Scholar 

  57. John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. Republished by the MIT press, 1992.

    Google Scholar 

  58. John H. Holland. Adaptation. In R. Rosen and F. M. Snell, editors, Progress in theoretical biology. New York: Plenum, 1976.

    Google Scholar 

  59. John H. Holland. Properties of the bucket brigade. In Grefenstette [51], pages 1–7.

    Google Scholar 

  60. John H. Holland. Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Mitchell, Michalski, and Carbonell, editors, Machine learning, an artificial intelligence approach. Volume II, chapter 20, pages 593–623. Morgan Kaufmann, 1986.

    Google Scholar 

  61. John H. Holland. Hidden Order: How Adaptation Builds Complexity. 1996.

    Google Scholar 

  62. John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and P. R. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, 1986.

    Google Scholar 

  63. John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard. Classifier Systems, Q-Morphisms, and Induction. Research Notes in Artificial Intelligence, pages 116–128. Pitman Publishing: London, 1989.

    Google Scholar 

  64. John H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman and F. Hayes-Roth, editors, Pattern-directed inference systems. New York: Academic Press, 1978. Reprinted in: Evolutionary Computation. The Fossil Record. David B. Fogel (Ed.) IEEE Press, 1998. ISBN: 0-7803-3481-7.

    Google Scholar 

  65. John H. Holmes. Evolution-Assisted Discovery of Sentinel Features in Epidemiologic Surveillance. PhD thesis, Drexel Univesity, 1996. http://cceb.med.upenn.edu/holmes/disstxt.ps.gz.

  66. John H. Holmes. Discovering Risk of Disease with a Learning Classifier System. In Bäck [3]. http://cceb.med.upenn.edu/holmes/icga97.ps.gz.

    Google Scholar 

  67. John H. Holmes. Differential negative reinforcement improves classifier system learning rate in two-class problems with unequal base rates. In Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998 Koza et al. [83], pages 635–642. http://cceb.med.upenn.edu/holmes/gp98.ps.gz.

    Google Scholar 

  68. John H. Holmes. Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 243–264. (this volume).

    Google Scholar 

  69. Keith J. Holyoak, K. Koh, and Richard E. Nisbett. A Theory of Conditioning: Inductive Learning within Rule-Based Default Hierarchies. Psych. Review, 96:315–340, 1990.

    Article  Google Scholar 

  70. Jeffrey Horn and David E. Goldberg. Natural Niching for Cooperative Learning in Classifier Systems. In David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors. Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 1996. MIT Press Koza et al. [84], pages 553–564.

    Google Scholar 

  71. Jeffrey Horn, David E. Goldberg, and Kalyanmoy Deb. Implicit Niching in a Learning Classifier System: Nature’s Way. Evolutionary Computation, 2(1):37–66, 1994. Also IlliGAL Report No 94001, 1994.

    Article  Google Scholar 

  72. Dijia Huang. The Context-Array Bucket-Brigade Algorithm: An Enhanced Approach to Credit-Apportionment in Classifier Systems. In Schafier [125], pages 311–316.

    Google Scholar 

  73. Josep Maria Garrell i Guiu, Elisabet Golobardes i Ribé, Ester Bernadó i Mansilla, and Francesc Xavier Llorà i Fàbrega. Automatic Classification of mammary biopsy images with machine learning techniques. In E. Alpaydin, editor, Proceedings of Engineering of Intelligent Systems (EIS’98), volume 3, pages 411–418. ICSC Academic Press, 1998. http://www.salleurl.edu/~xevil/Work/index.html.

  74. Josep Maria Garrell i Guiu, Elisabet Golobardes i Ribé, Ester Bernadó i Mansilla, and Francesc Xavier Llorà i Fàbrega. Automatic Diagnosis with Genetic Algorithms and Case-Based Reasoning. To appear in AIENG Journal, 1999. (This is an expanded version of Guiu98a).

    Google Scholar 

  75. John H. Holmes. Evaluating Learning Classifier System Performance In Two-Choice Decision Tasks: An LCS Metric Toolkit. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], page 789. One page poster paper.

    Google Scholar 

  76. Leslie Pack Kaelbling, Michael L. Littman, and Andew W. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 1996.

    Google Scholar 

  77. Tim Kovacs. Evolving Optimal Populations with XCS Classifier Systems. Master’s thesis, School of Computer Science, University of Birmingham, Birmingham, U.K., 1996. Also tech. report CSR-96-17 and CSRP-96-17 ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-17.ps.gz.

    Google Scholar 

  78. Tim Kovacs. XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions. In Roy, Chawdhry, and Pant, editors, Soft Computing in Engineering Design and Manufacturing, pages 59–68. Springer-Verlag, London, 1997. ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/index.html.

    Google Scholar 

  79. Tim Kovacs. Deletion schemes for classifier systems. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 329–336. Also technical report CSRP-99-08, School of Computer Science, University of Birmingham. http://www.cs.bham.ac.uk/~tyk.

    Google Scholar 

  80. Tim Kovacs. Strength or Accuracy? Fitness calculation in learning classifier systems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 143–160. (this volume).

    Google Scholar 

  81. Tim Kovacs and Pier Luca Lanzi. A Learning Classifier Systems Bibliography. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 323–350. (this volume).

    Google Scholar 

  82. John Koza. Genetic Programming. MIT Press, 1992.

    Google Scholar 

  83. John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998.

    Google Scholar 

  84. John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors. Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 1996. MIT Press.

    Google Scholar 

  85. J. Kruschke. ALCOVE: An exemplar-based connectionist model of category learning. Psychology Review, 99:22–44, 1992.

    Article  Google Scholar 

  86. Pier Luca Lanzi. Adding Memory to XCS. In Proceedings of the IEEE Conference on Evolutionary Computation (ICEC98). IEEE Press, 1998. http://ftp.elet.polimi.it/people/lanzi/icec98.ps.gz.

  87. Pier Luca Lanzi. An analysis of the memory mechanism of XCSM. In Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998 Koza et al. [83], pages 643–651. http://ftp.elet.polimi.it/people/lanzi/gp98.ps.gz.

    Google Scholar 

  88. Pier Luca Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation, 7(2):125–149, 1999.

    Article  Google Scholar 

  89. Pier Luca Lanzi. Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 337–344.

    Google Scholar 

  90. Pier Luca Lanzi. Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 345–352.

    Google Scholar 

  91. Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000.

    Google Scholar 

  92. Pier Luca Lanzi and Stewart W. Wilson. Optimal classifier system performance in non-Markov environments. Technical Report 99.36, Dipartimento di Elettronica e Informazione-Politecnico di Milano, 1999. Also IlliGAL tech. report 99022, University of Illinois.

    Google Scholar 

  93. P.L. Lanzi and S. W. Wilson. Toward optimal classifier system performance in non-Markov environments. Evolutionary Computation, 2000. to appear.

    Google Scholar 

  94. Blake Lebaron, W. Brian Arthur, and R. Palmer. The Time Series Properties of an Artificial Stock Market. Journal of Economic Dynamics and Control, 1999.

    Google Scholar 

  95. Gunar E. Liepins, M. R. Hillard, M. Palmer, and G. Rangarajan. Credit Assignment and Discovery in Classifier Systems. International Journal of Intelligent Systems, 6:55–69, 1991.

    Article  Google Scholar 

  96. Gunar E. Liepins, Michael R. Hilliard, Mark Palmer, and Gita Rangarajan. Alternatives for Classifier System Credit Assignment. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 756–761, 1989.

    Google Scholar 

  97. Pattie Maes, Maja J. Mataric, Jean-Arcady Meyer, Jordan Pollack, and Stewart W. Wilson, editors. From Animals to Animats 4. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB96). A Bradford Book. MIT Press, 1996.

    Google Scholar 

  98. Ramon Marimon, Ellen McGrattan, and Thomas J. Sargent. Money as a Medium of Exchange in an Economy with Artificially Intelligent Agents. Journal of Economic Dynamics and Control, 14:329–373, 1990. Also Tech. Report 89-004, Santa Fe Institute, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  99. J. A. Meyer and S. W. Wilson, editors. From Animals to Animats 1. Proceedings of the First International Conference on Simulation of Adaptive Behavior (SAB90). A Bradford Book. MIT Press, 1990.

    Google Scholar 

  100. John H. Miller and John H. Holland. Artificial adaptive agents in economic theory. American Economic Review, 81(2):365–370, 1991.

    Google Scholar 

  101. Johann Mitlöhner. Classifier systems and economic modelling. In APL’ 96. Proceedings of the APL 96 conference on Designing the future, volume 26(4), pages 77–86, 1996.

    Article  Google Scholar 

  102. D. E. Moriarty, Alan C. Schultz, and John J. Grefenstette. Evolutionary algorithms for reinforcement learning. Journal of Artificial Intelligence Research, 11:199–229, 1999. http://www.ib3.gmu.edu/gref/papers/moriarty-jair99.html.

    MathSciNet  Google Scholar 

  103. Ichiro Nagasaka and Toshiharu Taura. 3D Geometic Representation for Shape Generation using Classifier System. pages 515–520. Morgan Kaufmann: San Francisco, CA, 1997.

    Google Scholar 

  104. Mukesh J. Patel and Marco Dorigo. Adaptive Learning of a Robot Arm. In Terence C. Fogarty, editor, Evolutionary Computing, AISB Workshop Selected Papers, number 865 in Lecture Notes in Computer Science, pages 180–194. Springer-Verlag, 1994.

    Google Scholar 

  105. Gregory J. E. Rawlins, editor. Proceedings of the First Workshop on Foundations of Genetic Algorithms (FOGA91). Morgan Kaufmann: San Mateo, 1991.

    Google Scholar 

  106. Robert A. Richards. Zeroth-Order Shape Optimization Utilizing a Learning Classifier System. PhD thesis, Stanford University, 1995. Online version available at: http://www-leland.stanford.edu/~buc/SPHINcsX/book.html.

  107. Robert A. Richards and Sheri D. Sheppard. Classifier System Based Structural Component Shape Improvement Utilizing I-DEAS. In Iccon User’s Conference Proceeding. Iccon, 1992.

    Google Scholar 

  108. Robert A. Richards and Sheri D. Sheppard. Learning Classifier Systems in Design Optimization. In Design Theory and Methodology’ 92. The American Society of Mechanical Engineers, 1992.

    Google Scholar 

  109. Robert A. Richards and Sheri D. Sheppard. Two-dimensional Component Shape Improvement via Classifier System. In Artificial Intelligence in Design’ 92. Kluwer Academic Publishers, 1992.

    Google Scholar 

  110. Robert A. Richards and Sheri D. Sheppard. A Learning Classifier System for Three-dimensional Shape Optimization. In H. M. Voigt, W. Ebeling, I. Rechenberg, and H. P. Schwefel, editors, Parallel Problem Solving from Nature — PPSN IV, volume 1141 of LNCS, pages 1032–1042. Springer-Verlag, 1996.

    Chapter  Google Scholar 

  111. Robert A. Richards and Sheri D. Sheppard. Three-Dimensional Shape Optimization Utilizing a Learning Classifier System. In David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors. Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 1996. MIT Press Koza et al. [84], pages 539–546.

    Google Scholar 

  112. Rick L. Riolo. Bucket Brigade Performance: II. Default Hierarchies. In John J. Grefenstette, editor, Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA87), pages 196–201, Cambridge, MA, July 1987. Lawrence Erlbaum Associates.

    Google Scholar 

  113. Rick L. Riolo. CFS-C: A Package of Domain-Independent Subroutines for Implementing Classifier Systems in Arbitrary User-Defined Environments. Technical report, University of Michigan, 1988.

    Google Scholar 

  114. Rick L. Riolo. Empirical Studies of Default Hierarchies and Sequences of Rules in Learning Classifier Systems. PhD thesis, University of Michigan, 1988.

    Google Scholar 

  115. Rick L. Riolo. The Emergence of Coupled Sequences of Classifiers. In Schaffer [125], pages 256–264.

    Google Scholar 

  116. Rick L. Riolo. The Emergence of Default Hierarchies in Learning Classifier Systems. In Schaffer [125], pages 322–327.

    Google Scholar 

  117. Rick L. Riolo. Lookahead Planning and Latent Learning in a Classifier System. In S. W. Wilson, editors. From Animals to Animats 1. Proceedings of the First International Conference on Simulation of Adaptive Behavior (SAB90). A Bradford Book. MIT Press, 1990 Meyer and Wilson [99], pages 316–326.

    Google Scholar 

  118. Rick L. Riolo. Modeling Simple Human Category Learning with a Classifier System. In Richard K. Belew, editors. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91). Morgan Kaufmann: San Francisco CA, July 1991 Booker and Belew [15], pages 324–333.

    Google Scholar 

  119. Gary Roberts. A Rational Reconstruction of Wilson’s Animat and Holland’s CS-1. In Schaffer [125].

    Google Scholar 

  120. Gary Roberts. Dynamic Planning for Classifier Systems. In Forrest [44], pages 231–237.

    Google Scholar 

  121. George G. Robertson and Rick L. Riolo. A Tale of Two Classifier Systems. Machine Learning, 3:139–159, 1988.

    Google Scholar 

  122. Cédric Sanza, Christophe Destruel, and Yves Duthen. A learning method for adaptation and evolution in virtual environments. In 3rd International Conference on Computer Graphics and Artificial Intelligence, April 1998, Limoges, France, 1998.

    Google Scholar 

  123. Teresa Satterfield. Bilingual Selection of Syntactic Knowledge: Extending the Principles and Parameters Approach. Kluwer, Amsterdam, 1999. Uses a learning classifier system to model human learning in biligual situations.

    Google Scholar 

  124. Shaun Saxon and Alwyn Barry. XCS and the Monk’s Problems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 223–242. (this volume).

    Google Scholar 

  125. J. David Schaffer, editor. Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA89), George Mason University, June 1989. Morgan Kaufmann.

    Google Scholar 

  126. Dale Schuurmans and Jonathan Schaeffer. Representational Difficulties with Classifier Systems. In Schaffer [125], pages 328–333. http://www.cs.ualberta.ca/~jonathan/Papers/classifier.ps.gz.

  127. Sandip Sen. Improving classification accuracy through performance history. In Forrest [44], pages 652–652.

    Google Scholar 

  128. Sandip Sen. A Tale of two representations. In Proc. 7th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pages 245–254, 1994.

    Google Scholar 

  129. Sandip Sen. Modeling human categorization by a simple classifier system. WSC1: 1st Online Workshop on Soft Computing. Aug 19–30, 1996. http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/p020.html, 1996.

  130. Lingyan Shu and Jonathan Schaeffer. VCS: Variable Classifier System. In Schaffer [125], pages 334–339. http://www.cs.ualberta.ca/~jonathan/Papers/vcs.ps.gz.

  131. R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra. The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 285–302. (this volume).

    Google Scholar 

  132. Robert E. Smith. Memory Exploitation in Learning Classifier Systems. Evolutionary Computation, 2(3):199–220, 1994.

    Article  Google Scholar 

  133. Robert E. Smith, B. A. Dike, R. K. Mehra, B. Ravichandran, and A. El-Fallah. Classifier Systems in Combat: Two-sided Learning of Maneuvers for Advanced Fighter Aircraft. In Computer Methods in Applied Mechanics and Engineering. Elsevier, 1999.

    Google Scholar 

  134. Robert E. Smith and David E. Goldberg. Reinforcement Learning with Classifier Systems: Adaptive Default Hierarchy Formation. Technical Report 90002, TCGA, University of Alabama, 1990.

    Google Scholar 

  135. Robert E. Smith and David E. Goldberg. Variable Default Hierarchy Separation in a Classifier System. In Rawlins [105], pages 148–170.

    Google Scholar 

  136. Wolfgang Stolzmann. An Introduction to Anticipatory Classifier Systems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 175–194. (this volume).

    Google Scholar 

  137. Wolfgang Stolzmann and Martin Butz. Latent Learning and Action-Planning in Robots with Anticipatory Classifier Systems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 303–320. (this volume).

    Google Scholar 

  138. R. S. Sutton. Learning to predict by the methods of temporal differences. In Machine Learning 3, pages 9–44. Boston: Kluwer, 1988.

    Google Scholar 

  139. Richard S. Sutton. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the Seventh International Conference on Machine Learning, pages 216–224, Austin, TX, 1990. Morgan Kaufmann.

    Google Scholar 

  140. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning — An Introduction. MIT Press, 1998.

    Google Scholar 

  141. R.S. Sutton, D. Precup, and S. Singh. Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artificial Intelligence, 112:181–211.

    Google Scholar 

  142. S. B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Džeroski, S. E. Fahlman, D. Fisher, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R. S. Michalski, T. Mitchell, P. Pachowicz, Y. Reich, H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, and J. Zhang. The MONK’s problems: A performance comparison of different learning algorithms. Technical Report CS-91-197, Carnegie Mellon University, Pittsburgh, PA, 1991.

    Google Scholar 

  143. Andy Tomlinson and Larry Bull. A Corporate Classifier System. In A. E. Eiben, T. Bäck, M. Shoenauer, and H.-P Schwefel, editors, Proceedings of the Fifth International Conference on Parallel Problem Solving From Nature — PPSN V, number 1498 in LNCS, pages 550–559. Springer Verlag, 1998.

    Chapter  Google Scholar 

  144. Andy Tomlinson and Larry Bull. On Corporate Classifier Systems: Increasing the Benefits of Rule Linkage. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 649–656.

    Google Scholar 

  145. Andy Tomlinson and Larry Bull. A zeroth level corporate classifier system. pages 306–313, 1999.

    Google Scholar 

  146. Andy Tomlinson and Larry Bull. A Corporate XCS. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 194–208. (this volume).

    Google Scholar 

  147. Kirk Twardowski. Credit Assignment for Pole Balancing with Learning Classifier Systems. In Forrest [44], pages 238–245.

    Google Scholar 

  148. Nicolaas J. Vriend. On Two Types of GA-Learning. In S.H. Chen, editor, Evolutionary Computation in Economics and Finance. Springer, 1999. in press.

    Google Scholar 

  149. Nicolaas J. Vriend. The Difference Between Individual and Population Genetic Algorithms. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999 Banzhaf et al. [4], pages 812–812.

    Google Scholar 

  150. Nicolaas J. Vriend. An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses. Journal of Economic Dynamics and Control, 24:1–19, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  151. Christopher J.C.H. Watkins. Learning from Delayed Rewards. PhD thesis, King’s College, 1989.

    Google Scholar 

  152. C.J.C.H. Watkins. Learning from delayed reward. PhD Thesis, Cambridge University, Cambridge, England, 1989.

    Google Scholar 

  153. Thomas H. Westerdale. An Approach to Credit Assignment in Classifier Systems. Complexity, 4(2), 1999.

    Google Scholar 

  154. Stewart W. Wilson. Knowledge Growth in an Artificial Animal. In Grefenstette [51], pages 16–23. Also appeared in Proceedings of the 4th Yale.

    Google Scholar 

  155. Stewart W. Wilson. Classifier Systems and the Animat Problem. Machine Learning, 2:199–228, 1987. Also Research Memo RIS-36r, the Rowland Institute for Science, Cambridge, MA, 1986.

    Google Scholar 

  156. Stewart W. Wilson. Bid Competition and Specificity Reconsidered. Complex Systems, 2(6):705–723, 1988.

    MATH  MathSciNet  Google Scholar 

  157. Stewart W. Wilson. Classifier System mapping of real vectors. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92), 1992. October 6–8, NASA Johnson Space Center, Houston, Texas.

    Google Scholar 

  158. Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1–18, 1994. http://prediction-dynamics.com/.

    Article  Google Scholar 

  159. Stewart W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149–175, 1995. http://prediction-dynamics.com/.

    Article  Google Scholar 

  160. Stewart W. Wilson. Explore/exploit strategies in autonomy. In Maja J. Mataric, Jean-Arcady Meyer, Jordan Pollack, and Stewart W. Wilson, editors. From Animals to Animats 4. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB96). A Bradford Book. MIT Press, 1996 Maes et al. [97], pages 325–332.

    Google Scholar 

  161. Stewart W. Wilson. Generalization in the XCS classifier system. In Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998 Koza et al. [83], pages 665–674. http://prediction-dynamics.com/.

    Google Scholar 

  162. Stewart W. Wilson. Get real! XCS with continuous-valued inputs. In L. Booker, Stephanie Forrest, M. Mitchell, and Rick L. Riolo, editors, Festschrift in Honor of John H. Holland, pages 111–121. Center for the Study of Complex Systems, 1999. http://prediction-dynamics.com/.

  163. Stewart W. Wilson. Get Real! XCS with Continuous-Valued Inputs. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 209–220. (this volume).

    Google Scholar 

  164. Stewart W. Wilson. State of XCS Classifier System Research. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [91], pages 63–82. (this volume).

    Google Scholar 

  165. Stewart W. Wilson and David E. Goldberg. A Critical Review of Classifier Systems. In Schaffer [125], pages 244–255. http://prediction-dynamics.com/.

  166. Zhaohua Zhang, Stan Franklin, and Dipankar Dasgupta. Metacognition in Software Agents Using Classifier Systems. In AAAI-98. Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 83–88, Madison (WI), 1998. AAAI-Press and MIT Press.

    Google Scholar 

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Lanzi, P.L., Riolo, R.L. (2000). A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999). In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 1999. Lecture Notes in Computer Science(), vol 1813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45027-0_2

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