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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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.
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.
Thomas Bäck, editor. Proceedings of the 7th International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann: San Francisco CA, 1997.
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.
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.
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.
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).
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.
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.
Lashon B. Booker. Classifier Systems that Learn Internal World Models. Machine Learning, 3:161–192, 1988.
Lashon B. Booker. Triggered rule discovery in classifier systems. In Schaffer [125], pages 265–274.
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.
Lashon B. Booker. Representing Attribute-Based Concepts in a Classifier System. In Rawlins [105], pages 115–127.
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).
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.
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.
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.
P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4):261–283, 1989.
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.
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.
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.
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.
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.
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.
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.
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.
T.G. Dietterich. Hierarchical reinforcement learning with the maxq value function decomposition. Submitted for journal publication. Available at.
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.
Jean-Yves Donnart and Jean-Arcady Meyer. Hierarchical-map Building and Self-positioning with MonaLysa. Adaptive Behavior, 5(1):29–74, 1996.
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.
Marco Dorigo. Using Transputers to Increase Speed and Flexibility of Genetic-based Machine Learning Systems. Microprocessing and Microprogramming, 34:147–152, 1991.
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.
Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning, 19:209–240, 1995.
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.
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.
Marco Dorigo and Marco Colombetti. Robot Shaping: An Experiment in Behavior Engineering. MIT Press/Bradford Books, 1998.
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.
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.
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.
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.
Francine Federman and Susan Fife Dorchak. Information Theory and NEXT-PITCH: A Learning Classifier System. In Bäck [3], pages 442–449.
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.
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.
Stephanie Forrest, editor. Proceedings of the 5th International Conference on Genetic Algorithms (ICGA93). Morgan Kaufmann, 1993.
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.
Peter W. Frey and David J. Slate. Letter Recognition Using Holland-Style Adaptive Classifiers. Machine Learning, 6:161–182, 1991.
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.
D. E. Goldberg. Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3:493–530, 1989.
David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
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.
John J. Grefenstette, editor. Proceedings of the 1st International Conference on Genetic Algorithms and their Applications (ICGA85). Lawrence Erlbaum Associates: Pittsburgh, PA, July 1985.
John J. Grefenstette. Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms. Machine Learning, 3:225–245, 1988.
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.
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.
R. Hayes-Roth. Patterns of induction and associated knowledge acquisition algorithms. In Pattern Recognition and artificial intelligence. New York: Academic Press, 1976.
John H. Holland. Processing and processors for schemata. In E. L. Jacks, editor, Associative information processing, pages 127–146. New York: American Elsevier, 1971.
John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. Republished by the MIT press, 1992.
John H. Holland. Adaptation. In R. Rosen and F. M. Snell, editors, Progress in theoretical biology. New York: Plenum, 1976.
John H. Holland. Properties of the bucket brigade. In Grefenstette [51], pages 1–7.
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.
John H. Holland. Hidden Order: How Adaptation Builds Complexity. 1996.
John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and P. R. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, 1986.
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.
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.
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.
John H. Holmes. Discovering Risk of Disease with a Learning Classifier System. In Bäck [3]. http://cceb.med.upenn.edu/holmes/icga97.ps.gz.
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.
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).
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.
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.
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.
Dijia Huang. The Context-Array Bucket-Brigade Algorithm: An Enhanced Approach to Credit-Apportionment in Classifier Systems. In Schafier [125], pages 311–316.
Josep Maria Garrell i Guiu, Elisabet Golobardes i Ribé, Ester Bernadó i Mansilla, and Francesc Xavier Llorà i Fà brega. 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.
Josep Maria Garrell i Guiu, Elisabet Golobardes i Ribé, Ester Bernadó i Mansilla, and Francesc Xavier Llorà i Fà brega. Automatic Diagnosis with Genetic Algorithms and Case-Based Reasoning. To appear in AIENG Journal, 1999. (This is an expanded version of Guiu98a).
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.
Leslie Pack Kaelbling, Michael L. Littman, and Andew W. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 1996.
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.
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.
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.
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).
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).
John Koza. Genetic Programming. MIT Press, 1992.
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.
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.
J. Kruschke. ALCOVE: An exemplar-based connectionist model of category learning. Psychology Review, 99:22–44, 1992.
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.
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.
Pier Luca Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation, 7(2):125–149, 1999.
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.
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.
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.
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.
P.L. Lanzi and S. W. Wilson. Toward optimal classifier system performance in non-Markov environments. Evolutionary Computation, 2000. to appear.
Blake Lebaron, W. Brian Arthur, and R. Palmer. The Time Series Properties of an Artificial Stock Market. Journal of Economic Dynamics and Control, 1999.
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.
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.
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.
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.
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.
John H. Miller and John H. Holland. Artificial adaptive agents in economic theory. American Economic Review, 81(2):365–370, 1991.
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.
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.
Ichiro Nagasaka and Toshiharu Taura. 3D Geometic Representation for Shape Generation using Classifier System. pages 515–520. Morgan Kaufmann: San Francisco, CA, 1997.
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.
Gregory J. E. Rawlins, editor. Proceedings of the First Workshop on Foundations of Genetic Algorithms (FOGA91). Morgan Kaufmann: San Mateo, 1991.
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.
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.
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.
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.
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.
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.
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.
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.
Rick L. Riolo. Empirical Studies of Default Hierarchies and Sequences of Rules in Learning Classifier Systems. PhD thesis, University of Michigan, 1988.
Rick L. Riolo. The Emergence of Coupled Sequences of Classifiers. In Schaffer [125], pages 256–264.
Rick L. Riolo. The Emergence of Default Hierarchies in Learning Classifier Systems. In Schaffer [125], pages 322–327.
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.
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.
Gary Roberts. A Rational Reconstruction of Wilson’s Animat and Holland’s CS-1. In Schaffer [125].
Gary Roberts. Dynamic Planning for Classifier Systems. In Forrest [44], pages 231–237.
George G. Robertson and Rick L. Riolo. A Tale of Two Classifier Systems. Machine Learning, 3:139–159, 1988.
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.
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.
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).
J. David Schaffer, editor. Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA89), George Mason University, June 1989. Morgan Kaufmann.
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.
Sandip Sen. Improving classification accuracy through performance history. In Forrest [44], pages 652–652.
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.
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.
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.
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).
Robert E. Smith. Memory Exploitation in Learning Classifier Systems. Evolutionary Computation, 2(3):199–220, 1994.
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.
Robert E. Smith and David E. Goldberg. Reinforcement Learning with Classifier Systems: Adaptive Default Hierarchy Formation. Technical Report 90002, TCGA, University of Alabama, 1990.
Robert E. Smith and David E. Goldberg. Variable Default Hierarchy Separation in a Classifier System. In Rawlins [105], pages 148–170.
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).
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).
R. S. Sutton. Learning to predict by the methods of temporal differences. In Machine Learning 3, pages 9–44. Boston: Kluwer, 1988.
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.
Richard S. Sutton and Andrew G. Barto. Reinforcement Learning — An Introduction. MIT Press, 1998.
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.
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.
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.
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.
Andy Tomlinson and Larry Bull. A zeroth level corporate classifier system. pages 306–313, 1999.
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).
Kirk Twardowski. Credit Assignment for Pole Balancing with Learning Classifier Systems. In Forrest [44], pages 238–245.
Nicolaas J. Vriend. On Two Types of GA-Learning. In S.H. Chen, editor, Evolutionary Computation in Economics and Finance. Springer, 1999. in press.
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.
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.
Christopher J.C.H. Watkins. Learning from Delayed Rewards. PhD thesis, King’s College, 1989.
C.J.C.H. Watkins. Learning from delayed reward. PhD Thesis, Cambridge University, Cambridge, England, 1989.
Thomas H. Westerdale. An Approach to Credit Assignment in Classifier Systems. Complexity, 4(2), 1999.
Stewart W. Wilson. Knowledge Growth in an Artificial Animal. In Grefenstette [51], pages 16–23. Also appeared in Proceedings of the 4th Yale.
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.
Stewart W. Wilson. Bid Competition and Specificity Reconsidered. Complex Systems, 2(6):705–723, 1988.
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.
Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1–18, 1994. http://prediction-dynamics.com/.
Stewart W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149–175, 1995. http://prediction-dynamics.com/.
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.
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/.
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/.
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).
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).
Stewart W. Wilson and David E. Goldberg. A Critical Review of Classifier Systems. In Schaffer [125], pages 244–255. http://prediction-dynamics.com/.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-45027-0_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67729-1
Online ISBN: 978-3-540-45027-6
eBook Packages: Springer Book Archive