Anticipatory Learning Classifier Systems

List of Figures. List of Tables. Foreword. Preface. Acknowledgments. 1. Background. 2. ACS2. 3. Experiments with ACS2. 4. Limits. 5. Model Exploitation. 6. Related Systems. 7. Summary, Conclusions, and Future Work. Appendices. Appendix A: Parameters in ACS2. Appendix B: Algorithmic Description of ACS2. Appendix C: ACS2 C++ Documentation. Appendix D: Glossary. References. Index.

[1]  Richard S. Sutton,et al.  Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.

[2]  Wolfgang Stolzmann,et al.  An Introduction to Anticipatory Classifier Systems , 1999, Learning Classifier Systems.

[3]  Martin V. Butz,et al.  First Cognitive Capabilities in the Anticipatory Classifier System , 2000 .

[4]  Richard S. Sutton,et al.  Reinforcement learning architectures for animats , 1991 .

[5]  Pier Luca Lanzi,et al.  An Analysis of Generalization in the XCS Classifier System , 1999, Evolutionary Computation.

[6]  Eric B. Baum,et al.  Toward a Model of Mind as a Laissez-Faire Economy of Idiots , 1996, ICML.

[7]  Martin V. Butz,et al.  An algorithmic description of XCS , 2000, Soft Comput..

[8]  Wolfgang Stolzmann,et al.  Anticipatory Classifier Systems: An overview of applications , 2001 .

[9]  Jean-Arcady Meyer,et al.  Lookahead Planning and Latent Learning in a Classifier System , 1991 .

[10]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[11]  Pier Luca Lanzi,et al.  Learning classifier systems from a reinforcement learning perspective , 2002, Soft Comput..

[12]  R. Rescorla,et al.  Postconditioning devaluation of a reinforcer affects instrumental responding. , 1985 .

[13]  Martin V. Butz,et al.  Investigating Generalization in the Anticipatory Classifier System , 2000, PPSN.

[14]  A. Martin V. Butz,et al.  The anticipatory classifier system and genetic generalization , 2002, Natural Computing.

[15]  David E. Goldberg,et al.  The Race, the Hurdle, and the Sweet Spot , 1998 .

[16]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[17]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[18]  Martin V. Butz,et al.  Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System Part 2: Performa , 2000 .

[19]  Gilles Venturini,et al.  Adaptation in dynamic environments through a minimal probability of exploration , 1994 .

[20]  Pavel Brazdil,et al.  Experimental Learning Model , 1978, AISB/GI.

[21]  Marco Colombetti,et al.  Robot Shaping: An Experiment in Behavior Engineering , 1997 .

[22]  Martin V. Butz,et al.  Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems , 1999, Learning Classifier Systems.

[23]  Olivier Sigaud,et al.  YACS: Combining Dynamic Programming with Generalization in Classifier Systems , 2000, IWLCS.

[24]  Dave Cliff,et al.  Adding Temporary Memory to ZCS , 1994, Adapt. Behav..

[25]  Martin V. Butz,et al.  How XCS evolves accurate classifiers , 2001 .

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  M. Pelikán,et al.  Analyzing the evolutionary pressures in XCS , 2001 .

[28]  Dirk Thierens,et al.  Mixing in Genetic Algorithms , 1993, ICGA.

[29]  H. Crichton-Miller Adaptation , 1926 .

[30]  John J. Grefenstette,et al.  Credit assignment in rule discovery systems based on genetic algorithms , 1988, Machine Learning.

[31]  M. Minsky The Society of Mind , 1986 .

[32]  Robert E. Smith,et al.  A Study of Rule Set Development in a Learning Classifier System , 1989, ICGA.

[33]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[34]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[35]  Marco Colombetti,et al.  What Is a Learning Classifier System? , 1999, Learning Classifier Systems.

[36]  J. P. Seward An experimental analysis of latent learning. , 1949, Journal of experimental psychology.

[37]  T. Kovacs XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions , 1998 .

[38]  Lashon B. Booker,et al.  Improving the Performance of Genetic Algorithms in Classifier Systems , 1985, ICGA.

[39]  Stewart W. Wilson Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.

[40]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[41]  Martin V. Butz,et al.  Probability-Enhanced Predictions in the Anticipatory Classifier System , 2000, IWLCS.

[42]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[43]  David E. Goldberg,et al.  A Critical Review of Classifier Systems , 1989, ICGA.

[44]  Richard S. Sutton,et al.  Dyna, an integrated architecture for learning, planning, and reacting , 1990, SGAR.

[45]  Martin V. Butz,et al.  Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System - Part 1: Theoretical approach , 2000, GECCO.

[46]  Pier Luca Lanzi,et al.  A Roadmap to the Last Decade of Learning Classifier System Research , 1999, Learning Classifier Systems.

[47]  Joachim Hoffmann,et al.  Lernmechanismen zum Erwerb verhaltenssteuernden Wissens , 2000 .

[48]  E. C. Cherry Some Experiments on the Recognition of Speech, with One and with Two Ears , 1953 .

[49]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[50]  Richard S. Sutton,et al.  Model-Based Reinforcement Learning with an Approximate, Learned Model , 1996 .

[51]  D. Goldberg,et al.  New Challenges for an Anticipatory Classiier System: Hard Problems and Possible Solutions New Challenges for an Anticipatory Classiier System: Hard Problems and Possible Solutions , 2007 .

[52]  Eric B. Baum,et al.  Toward a Model of Intelligence as an Economy of Agents , 1999, Machine Learning.

[53]  Stewart W. Wilson Generalization in the XCS Classifier System , 1998 .

[54]  John H. Holland,et al.  Properties of the bucket brigade algorithm , 1985 .

[55]  Doina Precup,et al.  Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..

[56]  Stewart W. Wilson,et al.  Toward Optimal Classifier System Performance in Non-Markov Environments , 2000, Evolutionary Computation.

[57]  A. Goldman,et al.  Mirror neurons and the simulation theory of mind-reading , 1998, Trends in Cognitive Sciences.

[58]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[59]  D. Thistlethwaite A critical review of latent learning and related experiments. , 1951, Psychological bulletin.

[60]  P. Lanzi,et al.  Adaptive Agents with Reinforcement Learning and Internal Memory , 2000 .

[61]  Leslie Pack Kaelbling,et al.  Learning in embedded systems , 1993 .

[62]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[63]  A. Zeman Attentional Processing. The Brain's Art of Mindfulness , 1996 .

[64]  Satinder P. Singh,et al.  Reinforcement Learning with a Hierarchy of Abstract Models , 1992, AAAI.

[65]  R. Rescorla,et al.  Evidence for the hierarchical structure of instrumental learning , 1990 .

[66]  Pier Luca Lanzi,et al.  A Study of the Generalization Capabilities of XCS , 1997, ICGA.

[67]  Dana H. Ballard,et al.  Learning to Perceive and Act , 1990 .

[68]  Christopher Mark Witkowski,et al.  Schemes for learning and behaviour : a new expectancy model , 2013 .

[69]  John Garcia,et al.  Relation of cue to consequence in avoidance learning , 1966 .

[70]  Alan Bundy,et al.  An Analytical Comparison of Some Rule-Learning Programs , 1985, Artif. Intell..

[71]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[72]  D. JohnH.HOLLAN CONCERNING THE EMERGENCE OF TAG-MEDIATED LOOKAHEAD IN CLASSIFIER SYSTEMS , 2002 .